dataesr/scientific-paragraphs-categorization huggingface dataset:
As such, the applied uncertainty factors amounting to 100, do not necessarily lead to an overestimation of the risk. The CONTAM Panel noted that the derivation of relative potencies for TTX analogues is associated with a high level of uncertainty since the underlying methods and data are poorly described. Due to the fact that there are no occurrence data on TTX and its analogues in European marine gastropods and no consumption data, these could not be included in the exposure assessment and therefore constitutes an uncertainty. Because of the low number of consumption surveys, 5 out of 29, that allowed an estimation of the P95, the 95th exposures might be underestimated. Application of a 400 g portion size could both overor underestimate the high exposure. There is insufficient information about the distribution of TTX and its analogues in the different tissues of bivalves (i.
e. digestive gland versus whole flesh). Therefore, the CONTAM Panel could not take potential differences into account. There is also insufficient information about the variation in TTX levels between individual animals but it can be assumed that these differ significantly. However, a large portion size contains a large number of individual shellfish and as such may average out individual differences. This may not be the case for smaller portions. In the exposure assessment, occurrence data from samples collected following different sampling strategies are included. This could have led to the inclusion of non-representative samples meaning that the average and P95 levels are higher than if only samples from objective sampling would have been used leading to an overestimation of the actual exposures.
Due to a low number of quantified levels of TTX analogues, the relatively low levels of TTX analogues as compared to TTX when detected and the low relative potencies of the analogues detected, these were not included in the exposure assessment. This may lead to a small underestimation of the exposure. The occurrence data used in the exposure assessment came predominantly from the UK, and to a minor extent from the Netherlands and Greece and are therefore not representative for the EU which could have led to both an over or underestimation of the exposure. Additional occurrence data from the UK (Northern Ireland) and France could not be considered in the present exposure assessment because they were submitted late. As all the samples were non-detects, their inclusion would have resulted in lower TTX exposure.
Death is caused by respiratory failure and cardiac collapse. There is no antidote against TTX poisoning. • The doses at which TTX causes acute toxicity and lethality in humans are unclear, but there are a series of human case reports available that indicate that acute poisoning can result from doses of 4-42 lg/kg bw and higher. In the literature, a MLD of 2 mg per person is often mentioned (corresponding to 40 lg/kg bw in a 50-kg adult). The source of the MLD and the underlying data could not be retrieved. • For TTX analogues, no data are available allowing derivation of NOAELs or LOAELs; however, LD 50 values upon i. p. application have been reported.
Consideration of setting a HBGV • Based on the pronounced toxicity of TTX upon acute exposure, the CONTAM Panel decided to derive an ARfD. The use of human data, extrapolation from data on STX and the use of animal data were considered. Because of the overall limitations of the available human data, these were not used to derive the ARfD, but only as supportive information. Similar was true for STX which in humans shows similar effects and in mice similar toxic potency (both after i. p. and oral treatment) and mode of action as TTX. • Acute toxicity data on TTX in mice provide an acute LOAEL of 125 lg/kg bw (incidence 9/9 animals), and an acute NOAEL of 75 lg/kg bw based on apathy. However, there is a narrow interval between the dose where lethality occurred (250 lg/kg bw) and the dose where no apathy was observed.
4. 8. Occurrence data • A total of 8,268 analytical results for 1,677 samples were submitted to EFSA by the UK, Greece and the Netherlands. For all samples, analytical results were provided for TTX, whereas only 1,136 analytical results were provided for 4-epiTTX, 1,080 for 5,6,11-trideoxyTTX, 1,080 for 11-norTTX-6-ol, 1,080 for mono-deoxyTTX, 1,135 for 4,9-anhydroTTX and 1,080 for 11-oxoTTX. • Samples of bivalves were taken between 2006 and 2016, including mussels, oysters, cockles, clams, scallops and razor clams. No occurrence data were received for marine gastropods. • In 92% of the samples, TTX was not detected or quantified. LOQs on TTX ranged from 1 to 25 lg/kg, where LODs ranged from 0. 5 to 5 lg/kg. The UB median and 95th percentile levels reported to EFSA, taking into consideration all received data on TTX in bivalves were 5.
9 and 28 lg/kg, respectively. In most cases, this was based on raw meat, in some cases the digestive gland. • For different species of water molluscs, the highest upper bound mean was reported for clams, 10. 8 lg/kg and the highest 95th percentile level was reported for oysters, 79 lg/kg. • TTX analogues were detected in about 1% of the samples. 4. 9. Exposure assessment • Due to the relatively low number of quantified levels and relatively low levels of TTX analogues as compared to TTX and the low relative potencies of the analogues, the CONTAM Panel decided not to include them in the exposure assessment and only TTX data were used. • Because no occurrence and limited consumption data were available for marine gastropods, these could not be included in the exposure assessment. • Acute exposure assessments for TTX were carried out with two different approaches, namely i) by using the consumption data in the EFSA Comprehensive database applying a probabilistic approach, and ii) by using a large portion size of 400 g for bivalves.
Recommendations • More occurrence data on TTX and its analogues in edible parts of marine bivalves and gastropods from different EU waters are needed to provide a more reliable exposure assessment. • Occurrence data on marine gastropods are needed from different EU Member States. • Data on concentrations of TTX and its analogues should be obtained using EU approved and validated chemical-analytical methods. In addition, certified standards and reference materials for TTX and analogues are needed. • Information on the fate of TTX and its analogues during cooking is needed to refine exposure assessments. • Studies on the sources and critical factors leading to the accumulation of TTX in marine bivalves and gastropods are needed. • Further information on toxicokinetics of TTX and its analogues is needed.
The absence of consensus on the processes responsible for the interannual NAS winter bloom variations hence pleads for additional studies, especially now that longer remotely sensed and in situ time series are available. In the present paper, we hence aim at better assessing and understanding the interannual variability of the NAS winter bloom. On the observational side, this study benefits from the extended temporal coverage of the satellite chlorophyll data (∼ 15 years) and the advent of the Argo program, which allows monitoring in situ MLD and TCD variations from 2002 onwards. Performing a combined analysis of these datasets allowed us to perform a direct comparison between these. Section 4 provides a description of the interannual chlorophyll variability and its relationship with physical parameters, and discusses the mechanisms driving these fluctuations.
Observations The SChl estimates analysed in the present study are derived from different instruments (Sea-Viewing Wide Fieldof-View Sensor (SeaWiFS), Medium-Resolution Imaging Spectrometer (MERIS), and Moderate-Resolution Imaging Spectroradiometer (MODIS)) and retrieval algorithms, and they span different periods (Table  1 ). We will compare these different retrievals in Sect. 3. 1, in order to assess the robustness of remotely sensed data to investigate the NAS winter bloom. We used the Level 3 standard mapped images with a 9 × 9 km spatial and a monthly temporal resolution downloaded from http://oceancolor. gsfc. nasa. gov for all of these single-mission products. In addition, we also used three Level 3 merged ocean colour products downloaded from http://globcolour. info and http://www. oceancolour.
org/ at 4 × 4 km and monthly resolution: the weighted average empirical (AVW) product, the semi-analytical Garver-Siegel-Maritorena (GSM) product, and the Ocean Colour Climate Change Initiative (OC-CCI) product. The longest observational period is provided by the OC-CCI product and spans from October 1997 to December 2013. The ocean physical parameters are derived from an updated version of the dataset described in  Keerthi et al. (2013) , with an extended temporal coverage (2002 to 2013) and an estimate of the TCD in addition to that of the MLD. These MLD and TCD datasets are built from a combination of Argo and historical temperature and salinity profiles. To assess whether the mechanism proposed by Prasanna  Kumar et al. (2001; i. e. the Bermuda paradigm) dominates the interannual variability of the winter bloom in the northern AS over this extended period, we will investigate if there is a correlation between in-situ-derived interannual MLD and the satellite-derived interannual SChl anomalies.
We will test the alternative mechanism proposed by  Wiggert et al. (2002)  by investigating if there is a negative correlation between interannual in-situ-derived TCD anomalies and interannual satellite-derived SChl anomalies in the northern AS during winter. MLDs were estimated using a temperature criterion and are defined as the depth where the temperature decreases by 0. 2 • C with respect to the temperature at 10 m. The reference depth was taken at 10 m to avoid aliasing by the diurnal cycle. The TCD was defined as the depth of the maximal vertical temperature gradient. MLDs and TCDs were estimated from individual temperature profiles at their native vertical resolution. The resolution of the data was then degraded to a regular 2 • monthly grid, by taking the median of all MLDs and TCDs in each grid mesh.
A more detailed description of this procedure can be found in  Keerthi et al. (2013). An overview of the spatio-temporal coverage of this dataset over the NAS is provided in Fig. 2. While the data coverage is particularly sparse in winter before 2002 in our targeted region (e. g. less than 10 data per month are available in winter 2000 in the NAS region), the data density increased considerably after 2002, with the development of the Argo program (Fig. 2a ). After 2002, the NAS box winter data density ranged from 25 profiles per month during 2005 to nearly 120 profiles per month during 2012. This implies that the interannual MLD/TCD values averaged over the NAS box during winter 2002 to 2013 are built from an average of 100 to 500 individual values, giving us confidence in the robustness of the interannual MLD/TCD variability derived from this in situ dataset.
It should be noticed that the data coverage is however not spatially homogeneous, with the highest coverage along a shipping line crossing the NAS box (Fig. 2b ). We also use the World Ocean Atlas (WOA13) climatology  (Boyer et al. , 2013)  to derive climatologies of the thermocline and nitracline depths, calculated as the depths of maximum temperature and nitrate gradients, respectively. Wind speeds, surface air temperatures, and net heat fluxes are derived from the TropFlux product (Praveen  Kumar et al. , 2012). In order to assess the variability associated with various interannual climate modes, we have used standard climate indices. The El Niño-Southern Oscillation (ENSO) is represented using the Niño3. 4 index, which is the averaged sea surface temperature (SST) anomalies over the Niño3.
Phytoplankton growth is limited by five nutrients: NO 3 , NH 4 , PO 4 , SiO 4 , and Fe. The ratios among C, N, and P are kept constant for the living compartments, at values proposed by  Takahashi et al. (1985). On the other hand, the iron, silicon, and calcite pools of the particles are explicitly modelled. As a consequence, their ratios are allowed to vary. Nutrients are supplied to the ocean from five different sources: atmospheric dust deposition, rivers, sea ice, sediment mobilization, and hydrothermal vents. An interannually varying dust deposition dataset is not available to date. Dust deposition from the atmosphere is hence estimated from climatological monthly deposition maps simulated by the National Center for Atmospheric Research model  (Mahowald et al. , 2005) , assuming constant values for the iron content and solubility  (Moore et al.
, 2013; Akhil et al. , 2014 Akhil et al. , , 2016;; Praveen Kumar et al. , 2014) , including the AS  (Nisha et al. , 2013; Keerthi et al. , 2016). The simulation starts from rest, with temperature and salinity initialized from the WOA13. PISCES biogeochemical tracers are also initialized from the WOA13 database for nutrients and from the climatology of a global simulation for the other tracers  (Aumont and Bopp, 2006). After 5 years of spin-up with a climatological forcing, the model is forced with the Drakkar Forcing Set #4. 4 (DFS4. 4; Brodeau et al. , 2010)  from 1980 to 2012. This forcing is a modified version of the CORE dataset  (Large and Yeager, 2004 ), with atmospheric parameters derived from ERA-40 reanalysis  (Uppala et al. , 2005)  and ECMWF analysis after 2002 for latent and sensible heat flux computation.
Radiative fluxes are taken from the corrected International Satellite Cloud Climatology Project -Flux Dataset (ISCCP-FD) surface radiations  (Zhang et al. , 2004) , while precipitation forcing is a blend of satellite products described in  Large and Yeager (2004). All atmospheric fields are corrected to avoid temporal discontinuities and to remove known biases (see  Brodeau et al. (2010)  for details). In the following, we will analyse the 1993-2012 period.
Satellite SChl product intercomparison One of the major limitations of ocean colour imagery is the inability to perform accurate retrievals under clouds and in the presence of aerosols. In the AS, this is particularly challenging during the summer monsoon but not during winter, when the data coverage is larger than 90 % for all datasets considered and reaches 98 % in the case of the merged products (Table  1 ). Figure  3a  shows the SChl climatological sea- sonal cycle averaged over the NAS region for the six available satellite products. This figure reveals a good agreement between the different products in terms of seasonal phasing, with a semi-annual cycle associated with two seasonal blooms, one in summer (maximum in July, except for MERIS) and the other in winter (maximum in February for all products).
72, statistically significant at the 99 % confidence level; Fig. 9a ) and the model (0. 59 correlation significant at the 99 % confidence level; Fig. 9c ). In contrast, there is no statistically significant correlation at the 90 % confidence level between SChl and TCD variations over the same period for the two datasets (Fig. 9b, d ). The dependency of these relationships to the ocean colour product is shown in Table  2. This table indicates that all observational products display larger correlations between SChl and MLD than between SChl and TCD anomalies. The strength of the MLD-SChl relationship however varies depending on the product considered, with the largest correlation for MODIS (0. 86) and the weakest for the AVW product (0. 38, not significant at the 90 % significance level). None of the SChl prod-   10.
Summary The AS is one of the most productive regions in the world ocean, with a strong monsoon-driven seasonal cycle in SChl. The largest SChl bloom occurs during the summer monsoon in the western AS, in response to coastal and offshore upwelling driven by the Findlater jet. There is however also a prominent SChl bloom in winter in the northern AS, which exhibits large year-to-year fluctuations in its extent and intensity. These variations have not yet been described in detail, and there is no consensus on their driving mechanism. In this paper, we described the interannual NAS winter bloom variability and the mechanism driving this variability. To reach that goal, we combined the analysis of several observational datasets (remotely sensed chlorophyll products from various satellites and physical oceanic parameters derived from Argo in situ profiles) and a biogeochemical model simulation.
Our results reveal that SChl anomalies from the various satellite products exhibit a good phase agreement but large amplitude discrepancies. There is a strong (∼ ±50 % of the climatological value) year-to-year variability of the NAS winter SChl bloom. These fluctuations of the bloom amplitude are much better correlated (r ∼ 0. 4 to 0. 9 depending on the satellite product) with interannual fluctuations in MLD than with interannual fluctuations in TCD (r ∼ -0. 2 to 0. 1). As a result, correlations with interannual MLD anomalies are significant at the 90 % confidence level in four out of six chlorophyll satellite products but are insignificant for TCD anomalies irrespective of the product. The above analysis is based on a limited number of years in observations, due to the in situ data temporal coverage, which only becomes sufficient after 2002 thanks to Argo profilers.
Discussion The present study hence brings new insights on the interannual variability of the NAS winter bloom. As described in the Introduction, there have indeed been to date only two studies addressing the physical mechanisms controlling this interannual variability (Prasanna  Kumar et al. , 2001; Wiggert et al. , 2002) , which proposed different mechanisms relying on the analysis of a very limited number of winters. Our study allows demonstrating which mechanism dominates based on the analysis of much longer and various datasets (12 winters in observations and 20 winters in the model). Our observational and modelling results are both inconsistent with the hypothesis proposed by  Wiggert et al. (2002) , i. e. that the TCD in winter controls the bloom amplitude through a daily dilution effect.
In contrast, it is consistent with the hypothesis that interannual MLD variations largely control the amplitude of the bloom (Prasanna  Kumar et al. , 2001; Marra and Barber, 2005). On the observational front, our results rely on a comparison of interannual variations in satellite-derived SChl with in-situ-derived MLD and TCD variations. Those datasets are subject to uncertainties arising from both measurements ac- as dashed curves to ease comparisons between the 2 years. curacy and to sampling issues (i. e. the data density used to derive interannual NAS-averaged anomalies). The interannual satellite-derived SChl variations are likely to be very robust as the different satellite products in the NAS exhibit a very good phase agreement (Fig. 4 ) and a very good data coverage during the winter season (Table  1 ).
Regarding the in-situ-derived MLD and TCD products, the large number of individual measurements used to build seasonal anomalies (i. e. from 100 to 500 depending on the years consid- ered) yields a good accuracy on the estimate of these seasonal anomalies (∼ 2 m uncertainty on MLD and ∼ 3 m on TCD estimated from a Monte Carlo approach by subsampling available data, which is relatively small compared to the ∼ 20 m peak-to-peak amplitude of observed interannual variations of those fields). One of the major limitation of this in situ dataset may hence only be the inhomogeneous spatial sampling in the region considered, with a higher data density along a shipping line crossing the NAS box. The very good agreement between the in situ data and the totally independent model (for which averages are obtained over the entire NAS region, Fig.
7 ) however suggests that this is not the case. The fact that similar conclusions can be drawn from the model and the independent in situ dataset also strengthens the trust in each of those datasets. The diurnal cycle plays a key role in the hypothesis proposed by  Wiggert et al. (2002). Although the spatio-temporal coverage of our in situ dataset is sufficient to accurately sample interannual MLD variations, it does not allow monitoring the diurnal MLD variability. A proper investigation of the impact of diurnal variability on interannual chlorophyll variations from observations would hence require continuous and long-term temperature and chlorophyll profiles from a fixed location, which are not available to date. If the  Wiggert et al. (2002)  mechanism was dominating, there should however be a negative correlation between interannual variations of chlorophyll and TCD (i.
Regarding the impact on SChl, all observed and modelled chlorophyll products do exhibit a modest negative correlation between ENSO and interannual fluctuations of the winter bloom over the 2002-2011 period (Table  4 ). This is consistent with the hypothesis that an El Niño event drives a weaker winter monsoon, warmer surface air, less convective overturning, and hence a weaker bloom. The level of correlation is however modest and not statistically significant in all datasets (ranging from -0. 11 to -0. 44, depending on the SChl product), indicating that ENSO is not the only driver of the interannual winter bloom variations in this region. In addition, this influence of ENSO is not very stable in time (Table  4 ), with a larger correlation with 2 m air temperature over the recent 2002-2011 period (0.
As compared to the winter season, an analysis based on observations during the summer monsoon is complicated by the poorer satellite data coverage (Table  1 ) and the fewer Argo profiles in the Oman-Somalia upwelling region (Fig. 2b ). The model analysis may however provide further insights on the mechanisms that control the upwelling productivity during summer. Long-term variations also deserve further analysis. By analysing a 7-year-long satellite dataset,  Goes et al. (2005)  suggested that the southwest monsoon intensifies as a result of climate change, driving increased upwelling, primary production, and ecosystem changes in the AS. However, the shortness and discontinuity of the data call into question the reliability of these results  (Beaulieu et al. , 2013). Recent studies based on longer datasets  (Roxy et al.
Acknowledgements. This study was supported by the  Centre National d'Etudes Spatiales (CNES)  CLIMCOLOR project. M. G. Keerthi  is funded by the  IFCPAR (Indo-French Centre for Promotion of Advanced Research ) proposal 4907-1. Model experiments were performed at  HPC Pravah at CSIR-NIO. Matthieu Lengaigne ,  Christian Ethé ,  Jerome Vialard ,  Marina Levy ,  Clément de Boyer Montegut , and  Olivier Aumont  are funded by  Institut de Recherche pour le Développement (IRD). V. Parvathi is funded by  CSIR  under  SRF. I. Suresh  and  V. P. Akhil  acknowledge the financial support from  CSIR, New Delhi. We thank  Goddard GSFC/NASA  for providing the SeaWiFs, MERIS, and MODIS chlorophyll data;  ESA-GlobColour  for providing the GSM and AVW chlorophyll data; and  ESA Ocean Colour  for providing the OC-CCI chlorophyll data.
Data availability. MLD and TCD datasets are built from a combination of publicly available Argo and historical temperature and salinity profiles from the National Oceanographic Data Center (https://www. nodc. noaa. gov/). The OC-CCI (https://www. oceancolour. org) and SeaWiFs, MERIS, and MODIS (http://oceancolor. gsfc. nasa. gov) chlorophyll data; the Niño3. 4 index (http://www. cpc. ncep. noaa. gov/products/analysis_monitoring/ ensostuff/detrend. nino34. ascii. txt); the DMI index (http://www. jamstec. go. jp/frcgc/research/d1/iod/DATA/dmi. monthly. txt); WOA13 (https://www. nodc. noaa. gov/OC5/woa13/woa13data. html); and TropFlux (http://www. incois. gov. in/tropflux/) are publicly available. The AVW and GSM chlorophyll data from GlobColour data (http://globcolour. info) developed, validated, and distributed by ACRI-ST, France, are available.