dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: Bridging the re-processed SeaWiFS and MODIS datasets with CZCS The main objective of applying the "CZCS-like" algorithms to the SeaWiFS and MODIS observations was to achieve compatibility among the datasets from the three sensors. In a first step, we exhaustively verified the quality of the produced dataset to ensure that it was not degraded beyond scientific meaning (e. g. differences >100%, section 4. 1). This was achieved by comparing the NEW and the REF (SeaWiFS and MODIS) datasets using the statistical indices described in the previous section. First, we carried out a general comparison at the pan-Arctic scale (section 4. 1. 1) and second, extended the analysis to bioregional and seasonal scales (section 4. 1. 2). We also estimated the inter-mission bias between the NEW SeaWiFS and MODIS datasets based on their overlapping period  (2003) (2004) (2005) (2006) , section 4.
2) and corrected MODIS from possible bias. While the absence of overlap between CZCS and SeaWiFS hinders inter-mission bias correction, it cannot be overlooked. Although the intent of this work was to be as independent as possible from an in situ data collection, we also assessed the intermission bias by comparing the NEW dataset (CZCS, SeaWiFS and MODIS) with in situ measurements of Chl a, providing an additional quality assessment. Finally, climatological mean and decadal changes are presented in section 4. 3. The main outcomes of the section 4 will be discussed in the section 5.
General results at pan-Arctic scale The comparison between the entire REF and the NEW datasets for SeaWiFS and MODIS (i. e. all months, all years, all regions) are shown in Fig. 4. Left panels show the distribution of the chlorophyll a concentration in terms of the fraction of the AO area covered by concentration bins. Note that the CZCS cumulative distributions displayed in Fig. 4B  shows a reasonable agreement with the standard log-normal distributions in gray. The NEW processing does not substantially differ from the REF Chl a values for SeaWiFS and MODIS, and the three missions (CZCS in Fig. 4A-B , SeaWiFS in Fig. 4C -D and MODIS in Fig. 4E-F ) do have similar Chl a concentration averages (0. 81, 0. 89 and 0. 69 mg m -3 ) and distributions between 0. 03 and 30 mg m -3 (Fig. 4G-H ) when nearshore data are not masked.
However, small discrepancies occurred for some large Chl a values (i. e. , > 2 mg m -3 ) and are generally attributed to failures of the atmospheric correction or to the Morel-3 algorithm which is not adapted for coastal waters (see section 3. 2). After masking pixels (excluding [Chl a] > 15 mg m -3 and depth < 25 m) to remove possible outliers, the Chl a distributions remain similar between sensors evidencing a fair agreement between missions (0. 68, 0. 77 and 0. 57 mg m -3 for CZCS, SeaWiFS and MODIS respectively, Fig. 4I-J ) despite the persistence of slight discrepancies for large Chl a values (i. e. , Chl a > ~5 mg m -3 ). The cumulative distributions showed that Chl a concentrations greater than 15 mg m -3 , which were masked in the final implementation of the NEW dataset, represented less than 1% of the entire AO surface area independently of the sensor used (Fig.
Spatio-temporal impacts of the re-processed Chl a fields for SeaWiFS and MODIS A suite of statistical indices was examined to assess the impacts of the re-processing on the Chl a estimation in time (seasonally and annually) and space (bio-regionally) for SeaWiFS and MODIS. Such a detailed description was needed to identify the strength and limitations of the NEW dataset that extends to the CZCS era. Confirmation of the robustness and reliability of the NEW dataset is a necessary step prior to the calculation of long-term differences and the subsequent biological interpretations. For the sake of clarity, we limited the description of the statistical indices to the most striking results for some bio-regions. The reader can refer to the detailed statistical indices for any given bioregion in the supplementary material (statistical indices are presented in Tables  S1 to S6 , mean climatological values are presented in Table  1 ).
7%). During the more recent MODIS period, the decrease in sea-ice cover and subsequent increase in data coverage (11. 9%) resulted in a better precision and accuracy. Those differences between the NEW and REF datasets for both Sea-WiFS (Fig. 5 ) and MODIS (Fig. 6 ) were attributed to both the presence of variability (Δ u ) and bias (δ) in the Chl a signal which followed similar seasonal spatial dynamics than the MAPD. The largest differences were found where Chl a varied the most, i. e. in the sub-Arctic regions in spring and in the northernmost regions in summer. As an example, in the eastern Bering Sea, precision was lower in spring (Δ u = 0. 13 for Sea-WiFS; Δ u = 0. 09, for MODIS) than in summer (JAS; Δ u = 0. 12, for SeaWiFS; Δ u = 0. 07, for MODIS, Fig. 5A-B, 6A-B ). The differences between the NEW and REF datasets were also explained by the presence of systematic bias in Chl a (δ, see Fig.
In contrast, SeaWiFS was characterized by seasonal negative bias (δ = -4. 5 10 -2 in spring; δ = -3. 2 10 -2 in summer). ii) Impacts of the spatial variability at bio-regional scale on the reprocessing quality. In addition to the expected large-scale seasonal variations, spatial features were also associated with intrinsic bio-regional specificities (Fig. 1B ). Each bio-region has a unique bio-optical signature that results from topographic, local trophic interactions and biogeochemical fluxes such that the quality of the NEW dataset differs among regions. To avoid seasonal effects in the spatial analysis, bio-regional statistical indices.
Climatological inter-mission bias and comparison with in situ data The bio-regional correction was an important step to remove the inter-mission bias between SeaWiFS and MODIS for the NEW dataset and to provide consistent time-series that account for spatial heterogeneities. The temporal overlap between SeaWiFS and MODIS missions allowed the comparison of Chl a during the months of April to September between 2003 and 2006. Prior to the correction of the bias, the AO average difference between the two datasets was already low, but not negligible, with an estimated MAPD value of 16. 3%. The Chl a comparison between the two missions in terms of bias (δ MODIS-SeaWiFS ) highlighted the higher Chl a levels derived using SeaWiFS than those obtained using MODIS almost everywhere in the AO (negative bias in Fig.
7O  and 8 , see also Tables  S7-9 ). Averaged over the AO, the bias was more important in spring (δ MODIS-SeaWiFS = -9. 3 10 -2 , Fig. 8D ) than in summer (δ MODIS-SeaWiFS = -5. 6 10 -2 , Fig. 8E ) with an annual average of -7. 2 10 -2 (Fig. 8F ). In spring, the spatial distribution of δ MODIS-SeaWiFS was almost homogeneous throughout the AO. In summer, the largest δ MODIS-SeaWiFS values were located on the Russian Arctic shelves whereas the δ MODIS-SeaWiFS decreased everywhere else. All the MODIS climatologies (from daily to annual) were systematically corrected for the bias with the SeaWiFS dataset calculated on a pixel-by-pixel basis using the exact same time aggregation (e. g. spring climatologies were corrected with spring bias). The choice of correcting MODIS instead of SeaWiFS is further discussed in section 5.
From now on, the NEW dataset refers to the dataset that includes the bias-corrected MODIS dataset. The 2003-2006 climatologies of SeaWiFS and MODIS have also bioregional differences in precision as expressed by Δ u which cannot be corrected from one mission to another. However, the annual average Δ u over all bio-regions remained reasonably low (Δ u = 3. 4 10 -2 , red vertical line in Fig. 7N ). Seven bio-regions in total had distinctly higher Δ u than average (Fig. 7N , Table  S9 ). Among others, this list includes the Beaufort continental shelf (Δ u = 6. 7 10 -2 ) and the Laptev Sea (Δ u = 8 10 -2 ). Validation of satellite-derived properties is challenging due to the different spatial scales between satellite and in situ measurements and of the lack of matchups in the AO to provide results with statistical meaning.
The difficulty is amplified when assessing the performance of CZCS observations as they were collected about 40 years ago, when in situ Chl a pigments were generally measured using fluorescence-based or spectrophotometric methods without standardized procedures. Nevertheless, Chl a from the NEW dataset were compared with an in situ database made of all in situ Chl a found north of 45 • N in the World Ocean (https://www. ncei. noaa. gov/access/world-ocean-database-sele ct/dbsearch. html) and ICES (https://ocean. ices. dk/) databases. We excluded the European North and Baltic Seas which produced almost exclusively outliers likely due to their specific optical signature resulting from terrigenous inputs. In addition, results from the matchup analysis are affected by the quality of the fluorescence-estimated Chl a, which could not be warranted, and the reduced spatial resolution of the pixels used in the matchups (~28-km resolution), which was selected to increase the number of matchups for CZCS (from less than 10 with the 4km resolution to 147 with the 28-km resolution).
The match-up exercise shown in Fig. 9 , was carried out using 147, 3202 and 3775 available in situ and satellite Chl a pairs for CZCS, SeaWiFS and MODIS sensors, respectively. CZCS showed a significant Pearson's correlation r of 0. 54 (type II regression, p-value <10 -4 ) with a slope of 0. 72, similar to the slopes found for SeaWiFS (S = 0. 76) but smaller than the one found for MODIS (S = 0. 90). Both SeaWiFS and MODIS datasets showed similar correlation coefficients of about 0. 2. The intercept for CZCS (-0. 15) was greater than the one found for SeaWiFS and MODIS, which were close to zero (0. 047 and 0. 074 respectively).
Climatologies and decadal changes Seasonal (spring and summer) Chl a climatologies derived from the NEW dataset  (CZCS, 1979 (CZCS, -1984;; SeaWiFS, 1998 SeaWiFS, -2006;; and MODIS, 2007-2016 , corrected from bias) illustrate the spatial distribution of Chl a distribution and the shrinking sea-ice extent (seasonal climatology of minimum sea-ice concentrations >10%) recorded during the successive satellite missions (Fig. 10  and Table. 1 ). Indeed, the use of climatologies ensures 100% data coverage and missing pixels are only due to the presence of sea-ice (besides masked areas such as non-Arctic regions and depths shallower than 25 m). The Russian shelves were almost entirely ice covered during the CZCS era. The Chl a climatologies show similar spatial distributions for the three sensors with (i) the highest Chl a values observed in the inflow shelves (Barents and Bering Seas) in spring while the high AO is still covered with sea-ice, (ii) the highest Chl a values observed in summer in the Seasonal Ice Zone such as in the interior shelves (sensu  Carmack and Wassmann 2006; e.
Discussion The main outcome of the quality assessment of the NEW dataset, when compared to REF, is that the 'CZCS-like' re-processing of SeaWiFS and MODIS had limited impact on Chl a distribution over the AO. The differences (MAPD) averaged over the AO were about 20% and provides a good level of confidence. In comparison, satellite-derived Chl a in the Global Ocean usually show differences of ±35% relative to the reference value  (Bailey and Werdell, 2006; Hooker and Esaias, 1993; McClain et al. , 2006). The MAPD derived for the overlapping period between SeaWiFS and MODIS were about 16% (before correction) which corresponds to the lower bound of the usual range in Chl a difference found between SeaWiFS and MODIS in other studies (20-35%, e. g. Melin, 2010). The statistical indices computed to compare NEW and REF for all the 27 bio-regions (Fig.
7 ) showed, in general, high Spearman's correlation coefficients (r s > 0. 8), low unbiased RMS differences (<0. 1) and biases (< 0. 01). Those results confirmed a good agreement between the REF and the NEW datasets similar or even better performances than intersensors comparison in the global Ocean (e. g. Djavidnia et al. , 2010). In that sense, the NEW SeaWiFS and MODIS datasets do not seem particularly altered by the use of CZCS atmospheric correction and the application of the Morel-3 algorithm to derive Chl a. In future studies, it would also be interesting to compare the NEW dataset with the recently improved GSM-based algorithm (AO. GSM,  Lewis and Arrigo, 2020). The NEW dataset presents the unique advantage of expanding the temporal coverage of ocean color in the Arctic Ocean to the late 70's and early 80's.
The difference between the two periods illustrates the increase (red) or decrease (blue) in Chl a. The black background corresponds to regions with no data because of sea-ice, masked areas (depth < 25 m) or outside the studied area. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) uncertainties. Results from several bio-regions have to be interpreted with caution at both the seasonal and annual scale (see next section). Bio-regions with little amount of data and/or with terrestrial inputs showed lower correlation coefficient between the NEW and REF datasets (r s < 0. 75, Fig. 7 ). These bio-regions did not meet an acceptable level of confidence to be included in the analysis. Therefore, we chose not to interpret results from flagged regions such as the White Sea, the central Arctic in spring or the Laptev Sea in summer.
However, the results of the comparison between ocean color estimations and in situ observations were within the range found in the literature for current sensors (Fig. 9 ). The (type II) regression of satellite-derived against in situ Chl a showed a significant correlation (r 2 = 0. 54, p-value < 0. 01) with a slope of 0. 72, an intercept of -0. 15 and an unbiased RMS of about 0. 5. For comparison,  Lewis and Arrigo (2020)  tested on SeaWiFS and MODIS their new 'state-of-the-art' Arctic-adapted semi-analytical algorithm and found results similar r 2 ~ 0. 58 and RMS ~ 0. 46. Furthermore, in order to compensate for the low number of matchups for CZCS (N = 147), a bootstrap evaluation was performed by repeating 1000 times the regression (model II) with a random subsampling (75%) of the initial matchups dataset.
S6 ). In terms of magnitude, however, the MODIS-SeaWiFS inter-mission bias can be of similar or even greater magnitude than the changes in Chl a between the CZCS and SeaWiFS periods. This leads us to use caution in interpreting the results, as a possible uncorrected bias between SeaWiFS and CZCS may affect the magnitude or the sign of the Chl a changes (Fig. S7 ). ii) SeaWiFS and MODIS. The bias correction consisted of removing the climatological bias between the NEW SeaWiFS and MODIS datasets during their overlapping period  (2003) (2004) (2005) (2006). As a consequence, the percentage Chl a difference between the two datasets was divided by a factor of two  (MAPD decreased from ~16% to ~8% between 2003 and 2006, Fig. 8 ). In general, MODIS-derived Chl a estimations were slightly lower than those estimated from SeaWiFS.
The choice of correcting MODIS instead of SeaWiFS was motivated by the fact that the re-processed SeaWiFS NEW dataset had smaller biases than the MODIS NEW one (compared with REF, see Fig. 7 , 8D-E-F) and in line with previous multi-mission studies  (Gregg and Rousseaux, 2014; Mélin et al. , 2016). This choice was also supported by the comparison against in situ data as the correction of MODIS bias improved the matchups statistics by increasing the slope of the linear regression from 0. 78 to 0. 9 while the intercept remained unchanged. On the contrary, correcting SeaWiFS would have degraded the results by lowering the slope from 0. 76 to about 0. 64 (results not shown).
Decadal changes in Chl a The addition of the CZCS time series to the modern ocean color remote sensing era provides a valuable phytoplankton biomass baseline in the AO, and helps quantifying and understanding the changes that occurred before the triggering of the linear sea-ice loss during the late 1980s in the AO  (Peng and Meier, 2018). This glimpse into the recent past highlights interesting features in the Chl a decadal changes for the AO that are documented for the first time (Fig. 10):  (1) The first major outcome is that the exhaustive quality assessment of the dataset provided sufficient warranties to ensure reliable multi-annual variability analysis in most of the AO where data coverage was appropriate. (2) Chl a increased of about +24% over the AO since 1979. Our study therefore highlights that the increase previously documented from 1998 to current time actually started four decades ago and occurred throughout both in spring and summer.
Conclusion This study created the longest, compatible with global change studies, inter-sensor calibrated Chl a time series  (37-years, 1979-1984 and 1998-2016)  in the Arctic Ocean by connecting data from the first ocean color CZCS sensor with SeaWiFS and MODIS sensors  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016). This was achieved by: (1) re-processing the entire Sea-WiFS and MODIS time series with a similar atmospheric correction scheme than CZCS and applying the same Chl a algorithm (i. e. , two-band ratio) to the three sensors in order to ensure homogeneity and consistency of the data for global change study; (2) correcting SeaWiFS-MODIS inter-mission biases; and (3) assessing the quality of the re-processed dataset by comparison with recent remotely sensed time series as well as in situ data.
The quality assessment demonstrated that the reprocessing did not alter the quality of the dataset which is particularly well adapted for large scales and decadal studies. However, regional statistical analysis showed a spatial heterogeneity in the quality of the satellite-derived Chl a. A bio-regional approach provided a mean to exclude areas with unreliable data due to large discrepancies with a reference dataset. An important limitation of the CZCS dataset lies in its poor data coverage, which was addressed by a careful spatio-temporal aggregation. Despite those identifiable discrepancies, the addition of CZCS data to current time series offers several unique benefits including: the definition of baseline conditions in phytoplankton biomass in the Arctic Ocean for the early 1980s before the acceleration of sea-ice melt, a better characterization of decadal changes due to the longer period of observation (almost doubled), and further insight into the regional response of phytoplankton biomass to a changing Arctic Ocean.
Acknowledgements L. O. was funded by an  NSERC/CRSNG   "Visiting Fellowship" in Canadian laboratories  in the framework of the  DFO-International Governance Strategy  ( IGS  project, PI: Emmanuel Devred). L. O. was also supported by the  joint international laboratory Takuvik (ULaval/CNRS) , the  Canada Excellence Research Chair in remote sensing of Canada's new Arctic frontier. This study was also a contribution to the  Green Edge  project which is funded by the  following French and Canadian programs and agencies: ANR  (Contract # 111112 ),  CNES  (project # 131425 ),  IPEV  (project # 1164 ),  CSA ,  Fondation Total, ArcticNet, LEFE  and the  French Arctic Initiative  ( Green Edge  project). The authors would like to warmly thank  Sean Bailey  and  NASA OPGB Group  for assistance in the gathering of MODIS L1A files,  Maxime Benoit-Gagné  for the calculation of the Chl a with the GSM01 algorithm and  Martí Galí Tàpias  for the binning algorithm.
Data availability All the raw data used in this research are freely available to the public and may be downloaded through the links detailed in the Methods section. The quality-controlled databases (i. e. NEW dataset, seasonal and annual Chl a climatological composite maps, corrected from inter-mission biases) are publicly available from the SEANOE (SEA scieNtific Open data Edition) publisher at doi:10. 17882/86601 (Oziel et al. , 2022). The REF dataset is not publicly available and is processed and stored in Takuvik laboratory, and can be made available on request.
The helium porosity (ɸ H ) and Klinkenberg permeability (K K ) in the reservoir facies widely vary, ranging from 3. 0 to 22. 14% and 0. 003 to 8. 283 mD, respectively. However, the weak correlation between ɸ H and K K (R 2 = 0. 35) and petrographic datasets indicate limited diagenetic and mineralogical controls on the porosity-permeability (poroperm) parameters, consistent with relatively high heterogeneous reservoirs. We inferred that the heterogeneous reservoir properties and fluid flow in the studied analogue reservoir sandstones are dominantly influenced by depositional processes, as shoreface sandstones typically yield better reservoir quality than those deposited within the intertidal reaches. Our data indicate that diagenetic modification and bioturbation exerted lesser control on fluid-flow quality.
Numerous hydrocarbon reserves have been discovered in shallow marine reservoir sandstones (e. g. ,  Manzocchi et al. , 2008; Howell et al. , 2008). The shallow -coastal marine sandstone of the Nyalau Formation (Fig. 1 ) in the Tatau-Bintulu area of east Malaysia is a major and regionally extensive stratigraphic unit deposited by wave and tidal processes  (Amir Hassan et al. , 2013; Adepehin et al. , 2019a Adepehin et al. , , b, c, 2020)). The onshore Nyalau Formation is the equivalent of the petroleum-producing siliciclastic Cycles I and II successions in Sarawak offshore in the Balingian Province and Baram Delta Province  (Fui, 1978; Amir Hassan et al. , 2016; Lunt and Madon, 2017). Although published geochemical datasets indicate organic-rich shales and coals in the formation, the search for commercial hydrocarbon deposits in the Nyalau Formation in the study area-the southern part of Miri in the Baram Delta Province (Fig.
025/s. The samples were mildly powdered with an agate mortar and a pestle, packed in cavity mount, and analysed from the angular range of 2-65 • 2θ. Sample preparation for XRD analysis and mineral identification was done after  Brindley and Brown (1980)  and  Moore and Reynolds (1997). The X'pert High Score software was used for peak indexation, mineral identification, and background stripping. Relative mineral proportions in each sample were semi-quantitatively calculated using area ratios of distinctive peaks of individual minerals with quartz as an internal standard. Estimated mineral percentages are not empirical but are used for comparative mineral abundances. The clay minerals were identified and distinguished using the 001 d-spacing of their characteristic peaks on XRD patterns and their morphologies under SEM.
5 km) in the Bintulu area  (Adepehin et al. , 2020). Although depositional facies and cement were identified as the dominant control on porosity and permeability in the studied sandstone reservoir facies, we theorised that the onshore subsurface equivalents of the Cycles I-III would be significantly affected by the sediment compaction, owing to the overburden loads on the reservoir units. Notwithstanding the promising organic geochemical datasets of the Nyalau Formation, the poor reservoir quality establishes that nonconnected pore spaces within the carrier beds would constitute significant petroleum exploration risk in the study area. This is particularly true for facies associations 2 and 3 that are interpreted to have been deposited alongside mud-rich lithological components within the intertidal zone (Fig.