dataesr/scientific-paragraphs-categorization huggingface dataset:
PMRV claims have seldom been based on empirical findings  [10]. This may be due to the limited number of empirical PMRV studies, each with its own limitations, and few opportunities to test how PMRV affects existing REDD+ components. Involving other stakeholders. PMRV requires more than local people. Setting up the program, training, supervision, trouble shooting, quality control and data interpretation will all require a certain expertise. Local NGOs working with local people can facilitate and also relay reported data collected by/with local people to the national level ([22] Ã ). The government should aggregate data from subnational sites, integrate the data into the national database, validate the data collected and report the outcomes to the international level.
Integrating knowledge and information from multiple disciplines and sources Participatory MRV requires the collection of data at the local level and the use and integration of different sources of information. In some cases, using a combination of modern technology and local knowledge can benefit MRV. The use of GPS and smartphones, as well as biomass assessments, data analysis and reports writing skills can also contribute to MRV. Although local people know the land use history of their territory and the drivers of deforestation and forest degradation, this knowledge will still need to be standardized for it to be integrated into the national MRV system and databases. Data collection can be standardized as part of an integrative and interactive system that builds communication and sharing among the different stakeholders (e.
g. villagers, government MRV agencies, civil society). Pratihast et al. [27]  Ã describe the design and implementation of such a (web-based) system to monitor the UNESCO Kafa Biosphere Reserve in Southwestern Ethiopia. Information collected through this web-based system was consistent over time and aggregated in a standardized way. Their system supports PMRV because it is interactive, dependent on local monitoring data, and provides feedback to local people, something Ekowati et al. highlighted as vital for PMRV  [23]. DeVries et al. [28]  Ã give another example of knowledge integration from Ethiopia. They combined a stream of data from smart phones used by local people with Landsat time series to characterize and map deforestation and forest degradation. The locally recorded forestchanges offer deeper understanding of forest change processes recorded by satellites, especially the changes below the canopy.
Bong et al. [20]  emphasize the importance of local knowledge of deforestation and forest degradation dynamics at the local level. Integrating this local knowledge is vital for MRV success because it allows for site-specific assessment and monitoring of drivers of land use change. Vega Praputra et al. [22]  suggest using simple data formats, based on existing forest reporting systems, which are agreed by all stakeholders. This should include a clear description of community reporting responsibilities and benefits. Beaudoin et al. [25]  stress that MRV costs could be minimized by limiting the sampling area for ground-truthing to areas where remote sensing and participatory mapping data are inconsistent. This can save time and may trigger local interest and participation in ground checks (i.
Conveying knowledge and information across governance levels The main feature of PMRV is the inclusion of local people in contributing to the data flow used in a national MRV system, and-conversely-in benefiting from feedback from the national system on the state of the forests they manage. Information needs to be conveyed across multiple levels of governance. Vega Praputra et al. [22]  explored data flow in the Indonesian forestry sector, and found that locally collected forestry data (e. g. yields and diseases of smallholder forest crops, forest disturbances, logging activities) could be merged with the national database in a consistent manner. For this to happen, they note the need for facilitators to be present at the local level (e. g. , civil society organizations, government agencies, trained villagers).
These facilitators should understand the national MRV system, possess the skills to aggregate data at the village level (e. g. using appropriate software), and ensure data accuracy, completeness, consistency and comparability. McCall et al. [30]  Ã found that, in Mexico, community members with appropriate training and a balance of technical skills and experience can competently collect data needed for MRV. In the Indonesian healthcare system, villagers have for decades been taking measurements and reporting to the sub-district government health center, eventually reaching the national level  [23]. Data collected by villagers are considered accurate and are used by the national government to guide planning. As explained earlier, Pratihast et al. [27]  proposed a simple web-based system to integrate locally collected data into the national database in near real-time.
Incentives for maintaining local participation need to be developed and should incorporate important non-financial incentives and equity. For example, (i) plan the measurement activities with local people to ensure relevance to the local context (e. g. tenure arrangements, level of ecological knowledge, livelihood activities); and (ii) acknowledge the community's role and rights as forest managers and beneficiaries, and provide new job and livelihood opportunities for participants. 3. To be 'PMRV' ready, national governments need to invest in a reporting and verification system and a national database designed to integrate locally collected data. This should include: (i) building the capacity of facilitators, (ii) simplifying and clarifying reporting procedures across multiple levels of governance; (iii) providing mechanisms to clarify monitoring and reporting responsibilities, and benefits from PMRV, which are often related to local land rights and livelihoods; (iv) using a tiered system that accommodates different levels of reporting capacity across communities and countries involved in REDD+ to progressively achieve the gold standards; and (v) developing PMRV standards and step-by-step guidelines to be included in GOFC-GOLD methods and procedures  [7].
3. Operationalize reporting: practical tools are required to facilitate reporting from local to national levels. While we possess a good understanding of how local communities could be involved in data collection, and national databases are already operational in many countries implementing REDD+, we lack agreed formats for data collection and reporting, procedures for aggregating locally collected date and ensuring its inclusion in the national database. 4. Operationalize verification: practical approaches are required to facilitate verification. PMRV can be one among multiple approaches, together offering more reliable information than any one approach alone. Key to improved reliability is the recognition of discrepancies between data sets from different sources and effective actions to address them.
5. Move from a project scale to a national scale: developing a larger scale system for PMRV in REDD+ poses challenges. However, we can utilize long term experiences such as from the Indonesian healthcare monitoring system to learn how to build a viable, national, data collection and reporting system. 6. Test and refine PMRV: any PMRV implementation needs to build in learning and adaptation procedures. The pilot projects with REDD+ implementation are small scale and PMRV is not fully operational. A large-scale test of PMRV that includes collecting, reporting, and validating data, then integrating the data into a national database, and finally feedback would provide further lessons to guide these important developments.
The occurrence assessment for ZEN reported in the literature is consistent with data in the EFSA database. Concerning the occurrence of modified forms, phase II conjugated forms have been often reported in the recent literature, whereas no data have been received by EFSA. The co-occurrence of ZEN and its phase I and phase II modified forms is mainly described in cereals and products thereof. While milling may lead to a redistribution of ZEN and its modified forms in the final fractions, with a possible enrichment in middlings, there is no evidence of significant degradation by processing. Molar relative potency factors (RPFs) of the modified forms relative to ZEN were applied to occurrence levels of the respective ZEN metabolites according to the Opinion of the EFSA CONTAM Panel delivered in 2016.
The EFSA occurrence database contained no data on modified zearalenone; therefore occurrence was based on limited information reported in the literature. An estimation of the human dietary exposure and animal feed exposure compared with the exposure to the parent mycotoxins and assessments of the human and animal health risks was performed. Risk characterisation was done by comparing exposure scenarios with reference doses for the parent compounds. The CONTAM Panel identified several uncertainties and data gaps for modified mycotoxins and recommended re-assessing the animal health effects of zearalenone and fumonisins in order to set NOAELs/LOAELs for these compounds. Recently, the CONTAM Panel assessed the appropriateness to set a group health based guidance value for ZEN and modified forms.
Feed occurrence data and methodologies Following an European Commission mandate to EFSA, a call for annual collection of chemical contaminant occurrence data in food and feed, including ZEN, was issued by the former EFSA Dietary and Chemical Monitoring Unit (now DATA Unit)  3  in December 2010 with a closing date of 1 October of each year. European national authorities and similar bodies, research institutions, academia, food business operators and other stakeholders were invited to submit analytical data on ZEN in feed. At the time of receiving the request for the scientific opinion from the European Commission, no data on the modified forms of ZEN were available in the EFSA Chemical Occurrence database. The EFSA Evidence Management Unit (DATA Unit) invited European national authorities and similar bodies, research institutions and other stakeholders to submit data on the following ZEN modified forms in feed: ZEN-14-glucoside (ZEN14Glc), ZEN-14-sulfate (ZEN14Sulf), a-zearalenol (a-ZEL), b-zearalenol (b-ZEL), zearalanone (ZAN), a-zearalanol (a-ZAL), b-zearalanol (b-ZAL), a-ZEL-14-glucoside (a-ZEL14Glc) and b-ZEL-14-glucoside (b-ZEL14Glc).
The data submissions to EFSA followed the requirements of the EFSA Guidance on Standard Sample Description for Food and Feed (EFSA, 2010a); occurrence data were managed following the EFSA standard operational procedures (SOPs) on 'Data collection and validation' and 'Data analysis and reporting'. By the end of July 2016, a total of 17,706 samples of feed with analytical data on ZEN and its modified forms were available in the EFSA database. Data received after that date were not included in the data set used to estimate dietary exposure. Following the EFSA SOP on 'Data analysis and reporting' to guarantee an appropriate quality of the data used in the exposure assessment the initial data set was carefully evaluated applying several data cleaning and validation steps. Special attention was paid to different parameters such as 'Sampling strategy', 'Sampling year', 'Sampling country', 'Analytical methods', and the 'Reporting unit'.
The search strategy was designed to identify scientific literature dealing with methods of analysis, chemistry, occurrence in feed, animal exposure, toxicity, toxicokinetics and adverse effects in the different animal species. An overview of the search terms is given in Appendix B, Section B. 1. Literature search was not restricted to publications in English language; however, literature in other languages was only considered if an English abstract was available. A first general literature search was performed in December 2015 and has since been updated in May and December 2016 for the adverse effects of ZEN and modified forms in farm and companion animals. Web of Science 8  and PubMed 9  were identified as databases appropriate for retrieving literature for the present evaluation. The references resulting from the literature search were imported and saved using a software package (EndNote 10  ), which allows effective management of references and citations.
2. 6. Methodologies for dietary exposure assessment in animals Exposure to ZEN and its modified forms by livestock is a function of their concentration in their diets and the amount of the diet consumed. In the absence of a comprehensive database on the amounts or types of feed consumed by livestock in the EU, estimates of feed consumed for each of the main categories of farmed livestock and companion animals are based on published guidelines on nutrition (e. g. Caraba ño and Piquer, 1998; NRC, 2007a,b; Leeson and Summers, 2008; McDonald et al. , 2011; EFSA FEEDAP Panel, 2012; OECD, 2013) , together with expert knowledge of production systems in Europe. For many farmed livestock and companion animals, their nutritional requirements are provided in commercially manufactured complete (compound) feeds.
, 1987a) , which is the lowest reported NOAEL for mature female pigs. The database for boars is limited. Precocious spermatogenesis, damage to the germinal epithelium and interstitial cell hyperplasia were reported from young boars given feed containing 30 mg ZEN/kg, corresponding to 1. 2 mg/kg bw per day in the study, for varying time periods  (Vanyi and Szeky, 1980)  and reduced serum concentrations of progesterone and testosterone was observed in male piglets given 50 lg ZEN/kg bw per day for 22 days  (Jiang et al. , 2012).
, 2015). Nathanail et al. (2015)  reported for the first time the occurrence of ZEN16Glc in naturally infected cereals from Finland, together with ZEN, ZEN14Glc, ZEN14Sulf, aand b-ZEL, and aand b-ZEL14Glc. The sum of modified forms (phase I and phase II) exceeded ZEN up to 150% in barley (n = 34), while in oats and wheat they account for up to 50% in respect to the parent form. ZEN14Sulf was the most abundant modified form in Finnish cereals, reaching the highest amount in oats. Although extensive data on the occurrence of modified forms of ZEN in feed from plant origin are not available, data reported in the literature so far cover a wider range of modified forms compared to those received by EFSA and used for calculating the exposure. In particular, the EFSA database did not receive any data on phase II conjugates (i.
Dietary exposure assessment for farm and companion animals For all species, P95 and mean exposures have been estimated based on the 95th percentile and the mean LB and UB concentrations, respectively. According EFSA (2010b), caution is needed when calculating acute exposure (95th percentile) where data on less than 60 samples are available, since the results may not be statistically robust. Therefore, in this Opinion, estimates of acute exposure (95th percentile) have not been made where data on < 60 samples are available. Furthermore, mean LB and UB data for feeds derived from less than 10 samples have not been used in the estimates of exposure. Exposures by farmed livestock and companion animals are presented below for ZEN  (Section 3. 3. 3) and for the sum of ZEN, a-ZAL, b-ZAL, ZAN, a-ZEL, b-ZEL, applying the RPFs  (Section 3.
3. 4). For many livestock in Europe, feeds are supplied in the form of commercially produced speciesspecific blends or compound feeds, and for some species data on the ZEN + a-ZAL + b-ZAL + ZAN + a-ZEL + b-ZEL contents were provided by the European countries (see Section 3. 2. 1, Table  7 ). Where these data were available, P95 and mean exposures have been calculated using the concentrations reported and assumed intakes (see Appendix D). For those livestock categories for which insufficient data on species-specific compound feeds were provided (i. e. for less than 10 samples), the CONTAM Panel identified example diets and feed inclusion rates (see Appendix D for details), and used concentrations of ZEN + a-ZAL + b-ZAL+ ZAN + a-ZEL+ b-ZEL in individual feed materials to estimate P95 and mean exposure.
In contrast, data have been provided to EFSA on levels of ZEN and the modified forms in the grass hay, maize silage and cereal straws and these data have been used to estimate exposure in those ruminant feeding systems where these are the main forages. Two scenarios have been considered in estimating exposure by ruminant livestock to ZEN. In the first it has been assumed that species-specific compound feeds are fed together with fresh grass and/or grass silage, and in the absence of data on the presence of ZEN in these feeds it is assumed that the forages make no contribution to exposure. In the second scenario, grass hay has been assumed to be the sole forage, and levels of ZEN in grass hay have been applied to calculate exposure. Estimated P95 and mean exposures for high yielding dairy cows and fattening beef cattle to ZEN are presented in Table  8.
Ruminants and horses Estimates of P95 and exposures for high yielding dairy cows and fattening beef cattle to the sum of ZEN and the modified forms for which data are available a-ZAL, b-ZAL, ZAN, a-ZEL and b-ZEL (corrected for RPFs as described in Section 1. 3. 1, Table  1 ), derived from data on species-specific compound feed, are presented in Table  13. As in the previous section (Section 3. 3. 4. 1) two scenarios have been considered. In the first, it is assumed that the forage is either fresh grass or grass silage, and that these make no contribution to exposure, while in the second grass hay has been assumed to be the forage, and data on levels of ZEN plus the modified forms have been used. For other ruminant production systems, insufficient data on species-specific compound feeds were available, and therefore exposures were estimated using example rations and concentrations in individual feed materials (see Appendix D for details of rations used).
Risk characterisation There is limited knowledge on the effects of ZEN and its modified forms on farm and companion animals. Furthermore, there is no comprehensive database on feed consumption by livestock in the EU. It has therefore not been possible to fully assess the risks of ZEN and its modified forms for farm and companion animal health. However, for a number of farm livestock and companion animal categories the chronic exposure of ZEN in feed could be estimated at the mean and 95th percentile concentrations in animal diets based on expected feed intakes and example diets. Furthermore, exposure to the sum of ZEN and modified forms was calculated by use of molar RPFs of the modified forms relative to the oestrogenic activity of ZEN derived from information in rodents  (EFSA CONTAM Panel, 2016).
The estimated chronic exposure of ZEN at the UB mean and 95th percentile were 6 and 24% of the NOAEL, respectively. This NOAEL was based on reduced monocytes and granulocytes number, increased lipid peroxidation in liver and gill and altered metabolic activation. The Panel concluded that the estimated risk for chronic adverse health effects from feed containing ZEN was low for fish. For dogs, only a LOAEL was available. The estimated exposure of ZEN at the UB mean and 95th percentile were 1 and 3%, respectively, of the LOAEL. This LOAEL was based on myometrium and endometrium lesions, enlargement and atrophy of uterine glands, blood haematology and biochemistry. The Panel concluded that the estimated risk for chronic adverse health effects from feed containing ZEN was low for dogs. (a): Exposures have been calculated from dietary concentrations expressed on a fresh weight (88% dry matter) basis to make them comparable with the data from which NOAELS/LOAELS have been derived.
Uncertainty associated with occurrence and exposure Occurrence A final data set of 17,706 samples was available to estimate dietary exposure to ZEN and its modified forms. ZEN occurrence data were provided by 25 Member States, whereas data on the modified forms were provided by three Member States only. Therefore, there is large uncertainty on the possible country-based and year based differences in the levels of ZEN modified forms in diverse feed commodities. It is known that mycotoxin accumulation in crops is strongly affected by climate change. The last harvest seasons have been characterised by an increasing trend of contamination, due to exceptional climatic conditions. In particular, in 2014 several EU countries obtained a derogation to MLs for ZEN, DON, and fumonisins in maize. However, data in the EFSA database cover a long period  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  and have been collected from different geographic area, thus resulting in a decrease in the uncertainty.
The analytical results used for the exposure assessment were reported by different laboratories with varying LOQ/LODs. This, together with the large proportion of samples with left-censored data (i. e. ZEN about 60%, ZAN about 70%, a-ZEL and b-ZEL, and aand b-ZAL about 100%), is likely to account for these large differences. Furthermore, for certain feeds (particularly for poultry), no data were presented for a-ZAL, b-ZAL, ZAN, a-ZEL or b-ZEL. Consequently, while the LB values tends to underestimate the chronic dietary exposure to ZEN and its modified forms, the use of UB values tend to overestimate it (see Section 2. 1) and this therefore results in a large uncertainty to the overall estimates of dietary exposure. It should be noted that there is considerable uncertainty in the estimate of exposure for rabbits.
Uncertainties on the studies used for evaluation of adverse effects in farm and companion animals No toxicological data on ZEN were available for cats. For other animal species such as cattle, goats, horses, rabbit and mink the toxicological data were too limited to allow the establishment of reference points for ZEN. For fish, the only toxicological data were obtained for carps and therefore have to been extrapolated to all fish species. Similarly, for poultry, toxicological data were only available for chicken and turkey. This contributed to the overall uncertainty. For dogs, the experiment was performed on mixed breed animals, only LOAEL was determined and body weight of the animals was not reported. For pig, the key study was performed with naturally contaminated maize, containing not only ZEN but also other mycotoxins.
The highest number of reported samples for ZEN was available for 'Cereal grains, their products and by-products' (~67%) and in particular 'Wheat' (n = 6,499). Other well represented feed groups that were well represented were 'Complementary/Complete feed' (n = 2,625), 'Maize and corn' (n = 2,048) and 'Barley' (n = 1,596). Although an important ingredient in commercial rabbit diets, no data were reported for Lucerne meal. • The occurrence assessment for ZEN reported in the literature is consistent with data in the EFSA database. Concerning the occurrence of modified forms, phase II conjugated forms have often been reported in the recent literature, whereas no data have been received by EFSA. • The co-occurrence of ZEN and its phase I and phase II modified is mainly reported in cereals and products thereof.
Pigs and poultry Sufficient data for species-specific compound feeds or pigs, and for most categories of poultry (fattening chickens and turkeys, and for laying hens), were provided for zearalenone (Appendix D) and have been used to estimate exposure. No data were provided for a-ZAL, b-ZAL, ZAN, a-ZEL or b-ZEL in compound feeds for laying hens, fattening hens and fattening turkeys, and therefore, no estimates of exposure to ZEN plus the modified forms have been made for these poultry. No compound feed data were available specifically for gilts, and therefore the occurrence data for fattening pigs have been used to estimate exposure. In common with other poultry, ducks have limited ability to digest fibre, 16 and therefore, cereal grains form the major part of their diets. In Europe, wheat, maize and barley are most commonly used, with rye, sorghum triticale and oats used less widely.