dataesr/scientific-paragraphs-categorization huggingface dataset:
We elaborate and test a new baseline strategy which solves this problem and significantly reduces the number of outliers compared to the standard strategy commonly used for positioning (e. g. determination of national reference frame) in which the pre-defined network is composed of a skeleton of reference stations to which secondary stations are connected in a star-like structure. The new strategy is also shown to perform better than the widely used strategy maximizing the number of observations available in many GNSS programs. The reason is that observations are maximized before processing, whereas the final number of used observations can be dramatically lower because of data rejection (screening) during the processing. The study relies on the analysis of 1 year of GPS (Global Positioning System) data from a regional network of 136 GNSS stations processed using Bernese GNSS Software v.
Introduction and motivation Outliers and gaps in ZTD (zenith total delay) series estimated from ground-based Global Navigation Satellite System (GNSS) data are detrimental to meteorology and climate monitoring applications  (Bock et al. , 2016). Though GNSS data processing has been significantly improved over the years, outliers are still frequently observed in ZTD time series. This study aims at understanding the main factors leading to these outliers and testing improved processing strategies capable of minimizing these effects in the context of post-processing of GNSS data from moderately sized networks (e. g. national scale). We show that the baseline design strategy in a double-difference network processing has a strong impact on the quality and continuity of ZTD time series. Another approach of satellite data processing which can be used to estimate GPS (Global Positioning System) ZTD is the precise point positioning (PPP) technique.
Since PPP allows one to process each station individually, there is no direct propagation of errors between stations. However, the accuracy of ZTD estimates from PPP processing depends strongly on the quality of satellite orbits and clocks. In our study, we focused on improving the double-difference processing because most EPN and E-GVAP analysis centres rely Published by Copernicus Publications on behalf of the European Geosciences Union. on a network approach utilizing double-difference observations, and many of them use Bernese GNSS Software v. 5. 2  (Dach et al. , 2015). The most widely used baseline strategy in doubledifference processing is the so-called "obs-max" strategy which maximizes the number of double-difference observations from all stations of the network simultaneously  (Dach et al.
To circumvent this effect, other baseline designs have been introduced. Jaworski et al. (2011)  and  Bosy et al. (2012)  used a pre-defined network which contains a baseline skeleton of EPN reference stations, which serves as the nodes to which national stations are connected in a star-like geometry. They show that this strategy leads to accurate and stable coordinates for a national network. In this study we show that both these strategies have limitations and are prone to ZTD outliers and gaps. Investigation of various case studies helped us to identify the weaknesses of these strategies. We propose and test an alternative baseline strategy that overcomes the most severe limitations and yields more stable ZTD time series with less outliers and gaps. We also describe an efficient outlier detection method for the final screening of the reprocessed ZTD time series and assess the quality of final ZTD data by comparison with ERA-Interim reanalysis.
Initial processing strategy In this study, GPS data from 136 stations were analysed for the year 2014. They include 104 stations of the Polish national Ground Based Augmentation System (GBAS) network ASG-EUPOS and 32 EPN stations (remote and in Poland; Fig. 1 ). The remote EPN stations (i. e. with baselines longer than 500 km) were included in order to provide absolute ZTD estimates  (Duan et al. , 1996; Tregoning et al. , 1998). Details of the baseline strategy are given below. The processing was carried out for double-difference observations using Bernese GNSS Software v. 5. 2  (Dach et al. , 2015). A minimumconstraint approach was followed consistently with the general EUREF recommendations. The collected GNSS data were processed in 24 h sessions starting at 00:00 UTC each day with data sampling of 60 s.
, 2003). Also, the monthly differential code biases for satellites and receivers (P1-C1, P1-P2) were used. In the processing for all three strategies, a priori tropospheric delays were computed from the Global Pressure and Temperature (GPT) model combined with the Global Mapping Function (GMF;  Boehm et al. , 2006)  and Chen-Herring gradient model  (Chen and Herring, 1997). The ZTD parameters were modelled as piecewise linear functions of time and estimated every 2 h and the tropospheric gradients were estimated every 24 h, with absolute and relative constraints of 5 m. The carrier phase ambiguities were resolved using the recommended procedure with Bernese software, which applies several methods depending on the length of baselines, e. g. the SIGMA method and the Quasi-Ionosphere-Free algorithm (QIF;  Dach et al.
, 2015). Starting with this initial baseline structure, the network is modified automatically by the Bernese software every day, depending on the availability of observations. When observations for an EPN station are missing for the whole day, the obs-max solution automatically creates new baselines with nearby stations (see e. g. Fig. 4  discussed later). This type of baseline design works well for positioning but has one major drawback for tropospheric monitoring. Indeed, when observations for a given station are missing only for a fraction of a day, the station will still be processed and all baselines connected to this station will be impacted by the data gap. Since station coordinates are estimated daily, they are not impacted much by a short data gap (e. g. a few hours). In contrast, since ZTD parameters are estimated every 2 h, a data gap of about a few hours will strongly impact the ZTD estimates of all stations connected to the station with the gap.
At that time station USDL has a data gap which lasts until day 230 at 05:46 UTC. From 18:52 UTC to the end of the day 226, the cluster composed of EPN station SULP and its five ASG-EUPOS stations is disconnected from the main network and common mode ZTD biases show up. The formal errors increase simultaneously as a result of high correlation between the estimated ZTD parameters. Table  1  reports the values for ZTD and formal errors for two of the ASG-EUPOS stations (BILG and CHEL) connected to SULP, as well as the values for SULP and USDL. The ZTD bias at BILG, CHEL, and SULP varies as follows: +0. 10 m at 20:00 UTC compared to 18:00 UTC, -0. 14 m at 22:00 UTC compared to 20:00 UTC, and -0. 26 m at 00:00 UTC (day 227) compared to 22:00 UTC. The next day, USDL is not available and the network is changed automatically.
Comparison to obs-max solution Let us now quantify in a more statistical way the improvement of the new strategy compared to the old one and compare them both to the standard obs-max strategy taken as a reference. For this purpose, the same network has been reprocessed with Bernese software using the obs-max strategy for all stations (not distinguishing between EPN and ASG-EUPOS or between Polish and remote stations). As a measure of the quality of each of three solutions, we computed the standard deviations of the ZTD forward differences and of the formal errors for each station. Taking the forward dif- ferences of 2-hourly ZTD values allows us to remove almost completely the seasonal variations and at the same time to magnify the outliers. Large standard deviations are thus symptomatic of time series containing outliers.
Inspection of the design of the network in both processing variants (Fig. 11 ) shows that in obs-max solution, station GDAN was automatically connected to station WLAD and WLAD to EPN station REDZ, while in the new solution station GDAN was connected to EPN station REDZ (star structure). Table  3  shows the number of processed observations for the mentioned baselines with the two processing variants on day 170, before and after residual screening carried out automatically by Bernese software during processing. The numbers of observations are large before the screening for all the baselines, so there was no significant observation gap on that day. However, the numbers dropped strongly after screening, which reveals that observations were of bad quality, especially for station WLAD and to lesser extent for station GDAN.
Conclusions This study aims at understanding the main factors leading to outliers in GPS ZTD time series in a sub-regional network (typically a permanent national GNSS network). We show that the baseline design strategy in a double-difference network processing has a strong impact on the quality and continuity of ZTD time series. ZTD outliers are most of the time caused by sub-daily data gaps at reference stations which provoke disconnections of clusters of stations from the reference network and common mode biases due to the strong correlation between stations in short baselines. We developed an alternative baseline strategy that minimizes such disconnections and yields more stable ZTD time series with less outliers and gaps. The new strategy ensures that all the stations remain connected to the main reference network.
The range check and outlier check detect spikes in ZTD and formal errors based on constant and station-specific thresholds, respectively. The screening removed about 1. 2 % of ZTD estimates, which remains at an acceptable level when high data continuity is required. Finally, screened GPS ZTD were compared to ERA-Interim reanalysis to assess the quality of final ZTD data and detect smaller bias and jumps. We investigate the cases when the new strategy provides better results than obs-max solution. Although obs-max maximizes the number of double-difference observations from all stations of the network simultaneously, the baseline design is chosen by obs-max based on a priori number of observations. In case of bad quality of observations, the number may drop strongly after residual screening carried out during processing.
The improved processing strategy may also be an interesting approach for reprocessing historical data to generate new data with fewer outliers or to the operational processing to improve future ZTD estimates. More accurate and stable ZTD series may be produced in this mode, and the impact of equipment changes may be more easily detected in the double-difference residuals than in zero difference residuals. Some scientific applications also use GNSS tropospheric gradient estimates  (Dousa et al. , 2017)  which were not considered in this study. We argue that similar kind of outliers probably affect the gradient estimates and that the processing strategy proposed in this work would also similarly reduced them. The analysis of gradients and long time series will be considered in a future work.
Data availability. GPS processing results and all the results in the form of figures and tables for all types of presented comparisons and stations can be provided by request to the authors. The GPS RINEX data from the EUREF Permanent Network (EPN) stations are available from EPN Central Bureau (ftp://ftp. epncb. oma. be/pub/obs/). Six-hourly data from ERA-Interim reanalysis are available from the ECMWF website (http://apps. ecmwf. int/ datasets/data/interim-full-daily/levtype=sfc/).
The Inter-Stock Flexibility This flexibility mechanism (up to 9% of the TAC of a certain stock could be counted against another stock) was established in recognition that under relative stability, the shares of quota allocated to Member States could, in some cases, make it extremely difficult to comply with the Landing Obligation. Co-legislators, however, were very conscious of the risks of this mechanism, and they established two associated conditions: (a) that the stocks from which the catches are attributed to other stocks would be inside safe biological limitsto prevent the mechanism to result in higher catches of weak stocks and (b) that the real catches by species would be recorded, to avoid catch data being misleading. Catches that are caught in excess of quotas or for which the Member State has no quota, may be deducted from the quota of the target species provided that they: (i) do not exceed 9% of the quota of the target species and (ii) the stock of the non-target species is within safe biological limits (i.
Monitoring, Control and Enforcement Monitoring and enforcement are essential parts of the implementation of the Landing Obligation. Not only the credibility of the policy depends on effective enforcement, but also its expected positive effects will only be realised if adequate monitoring takes place. For example, the improvement of data (due to reporting on everything that is removed from the sea) is an expected side benefit of the Landing Obligation, but only if effective monitoring takes place. On the other hand, the flexibility mechanisms available will only be used to the full extent if the industry feels the pressure of enforcement; otherwise there would be little incentive to use such mechanisms, which would in turn give the impression that the whole policy is inapplicable. The move to managing catches instead of landings requires new forms of monitoring  (James et al.
, this volume) , and the practice of discarding is increasingly considered unacceptable from different points of view, including considerations about food security. Any possible future change in the policy is likely to be about adjustments to improve practical implementation and mitigate possible negative results, but a CFP with high levels of discarding will never return. Fitzpatrick, M. , Graham, N. , Rihan, D. J. , Reid, D. G. (2011). The burden of proof in co-management and results-based management: The elephant on the deck! ICES Journal of Marine Science, 68(8), 1656-1662. Fitzpatrick, M. , & Nielsen, K. N. (2016). DiscardLess Policy Brief No1: Year 1 of the Landing Obligation, key issues from the Baltic and Pelagic fisheries, 30 September 2016. https://doi. org/ 10. 5281/zenodo. 215155.
ICES Advice Scientific advice for fishing opportunities for North East Atlantic stocks is provided by ICES. The advice involves two processes. First, stock assessments estimate the current status of the stock and its historical development. Second, short-term forecasts estimate the catches in the following year corresponding to a given management target. The target is usually expressed in terms of a fishing mortality which reflects a broad management objective (management plans, MSY or Precautionary Approach). Stock assessments for the main commercial stocks are based on catch-atage estimates. Initially, catches comprised only landings, but discard estimates by fishing activity ("metier") have been increasingly available through improved sampling programs financed by the EU Data Collection Framework (Council Regulation (EC) No 199/2008).
There is also a requirement for vessel operators to record discard amounts for commercial species, however, limited compliance has meant that the log book data provide a gross underestimate of discard levels and reliance on these data is inadvisable  (STECF 2013). Because of the limited coverage of scientific discard sampling, estimates are not available for all stocks and métiers in all Member States. Informed procedures have thus been developed over time to raise estimates of discard ratios and allocate age structure to the unsampled strata, usually through a dedicated ICES Database called InterCatch. 1  In 2015, the scientific advice therefore included a complete provision for both landings and discards by métiers for most commercial stocks. The Landing Obligation poses a significant conceptual and technical challenge, and has rendered the provision of scientific advice more complex to perform and quality-check, and more difficult to formulate.
Since 2015, two main changes have been triggered in the ICES process, involving the way catch data are collected and the way forecasts are performed and presented. Changes in catch data have emerged from the need to quantify and raise new categories. It is still considered that even if landed, a part of the catch is still not targeted by fishermen, but they cannot be called discards anymore and are referred to as "unwanted catch", while landings are now referred to as "wanted catch". To keep track of these categories, the InterCatch database was expanded in November 2015, and now includes five categories: • L ¼ Landings. Landings above minimum size; • B ¼ BMS Landings. Landings below minimum conservation reference size, BMS. Relevant for stocks under the landing obligation. The BMS landing will consist of BMS landings and predator damaged fish.
2. STECF provided advice to the Commission on how to calculate TAC adjustments and have developed a methodology to facilitate these calculations (STECF 2015c, 2017c): • The ICES forecasts of catch and discards should be used as a starting point for calculating TAC adjustments consistent with FMSY principles. Fleet specific discard rates derived from the STECF database should then be used to determine fleet specific TAC adjustments. • For stocks where the entire fishery or clearly defined fleet segments, are subject to the landing obligation, and for which specific discard rates are available, then calculation of TAC adjustments can simply be based on the ICES catch forecast. • In situations where only a proportion of vessels (within a specific fleet segment) and that also meet a historic landing composition threshold are subject to the Landing Obligation, then the average ICES discard rate should be applied to reflect the proportion of vessels and landings covered under the landing obligation.
Moreover, the magnitude and pattern of the escape/release related mortality is far more challenging to quantify and monitor than discard related mortality. Furthermore, both ICES and STECF have consistently highlighted the lack of reporting by vessel operators of fish discarded under exemptions, discards of fish currently not subject to the Landing Obligation and catches of fish below MCRS, which compromises the quality of stock assessments and the accuracy of catch forecast. The quality of science supporting the implementation of the Landing Obligation would strongly benefit from provisions that strengthen data collection in this respect (e. g. through innovative monitoring measures such as Remote Electronic Monitoring, see Chap. 18). STECF concluded that if the data situation does not improve and the true quantities being caught as reported do not reflect the actual removals, then this will have a significant impact on the quality of scientific advice and, without a precautionary approach in setting fishing opportunities, a key objective of the CFP in achieving MSY may be compromised.
Once the user logs into the GIS platform, the general layer information is displayed in a small, interactive box. The modeling process starts when the user performs a query which generates a temporal fishing data subset extracted from the general SQL database stored in the system. This temporal fishing data subset collects information regarding the latitude, longitude, and species-specific information on the weight caught and weight discarded of each fishing operation performed by the fleet in the selected area and time period. Explanatory variables used for the analysis include bathymetry, slope, distance to the coast, sea bottom characteristics, Sea Surface Temperature and Chlorophyll-a concentration. Other important explanatory variables, such as the season, fleet characteristics, etc. , are intrinsic and selfcontained in the fishing data used to perform the modelling.
4. Reduced requirements for manual entry of logbook data into central databases. The defined area, time scale, and review period may not be the same for different legal obligations, policy and management goals, and operational objectives. The validity of the goals and objectives should be assessed by SMART criteria (Specific, Measurable, Achievable, Realistic and Time-bound). Policy approaches can be top-down (imposed by government), bottom-up (meeting popular demands from end users), or a combination of the two. The balance between these policy approaches will give an indication of how likely end-users will be to follow enforcement laws. A real challenge is to avoid duplication of effort by different public agencies and levels of government in MSP activities. It is necessary to provide a rational basis for setting priorities, as well as to direct resources to where and when they are needed most.
Decision Support Tools Using Observer Data Observer data come from on-board observers on commercial fishing vessels, and, like surveys, similar coverage is carried out across the EU. Their primary task is to record discards, but they also record fish that go to landings. Thus they represent very detailed information on catches, landed and discarded. It is only possible to deploy observers on a small proportion of all fishing trips, but we were able to combine observer data from France, Ireland and the UK for the Celtic Sea to provide a larger dataset to work on, and some of the results are shown here. Two different approaches are presented as examples of what information can be produced.
However, there is a need to continue to build on them, extend their geographic range and broaden their scope to more gear types (to date they are almost entirely for towed demersal gears). In the first instance, this could be done by linking them to the factsheets that have been produced in some of the regional gear development studies, which have taken place in many countries. There is also a need to ensure the longer-term support and survival of these databases and to secure commitments from relevant bodies (such as ICES, International Council for the Exploration of the Sea or the FAO, United Nations' Food and Agriculture Organisation) to resource them beyond the life of the projects within which they have been developed. Where sufficient data exists, there may be scope to combine the results from a number of trials to explore a broad range of selective gear options for use in a fishery.
Self-Sampling Another solution for the MCS of unwanted catches is the use of data collected or sampled by fishers. Information collected and reported are typically related to catch (total catch and catch composition) and fishing activity (location, duration of fishing activity). Fishers may also be required to take samples, such as tissue samples and otoliths, from the catch  (Pennington and Helle 2011). Data may be recorded electronically or on paper and entered into a database upon return. This information can then be processed and incorporated in stock assessments and management purposes, therefore having a role in the control of fishing activity. Self-sampling and recording by fishers is a technique used for data collection in a variety of fisheries, and may be mandatory through legislation.
Data and Analysis The data are collected through the LH inspections together with the declared catches by category (i. e. discards, landed catches below and above the minimum conservation reference size, BMS and LSC, respectively). EFCA compiles these data provided by the MS concerned, which allows completing and updating a matrix of discard ratios by segment and by period, which serves as a baseline for future analysis. In combination with the LH data collected during inspections, the datasets from each fleet segment are compiled based on information on annual quantities reported in logbooks provided by MS and aggregated by: (a) Month: from January to December. (b) Fleet segment: fleet defined based on the gear, mesh size and area of fishing activity. In some case the target species is an additional component to define the fleet segment.