dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, a new pixel-classification algorithm (Idepix) was implemented for MERIS data processing in v1 and for MERIS and SeaWiFS in v2, with L2Gen being used for the other sensors. The unusable pixels detected included three types of pixel classes: cloudy pixels, sea ice pixels, and pixels of mixed surface types, that is, those which include portions of land in their footprints. In subsequent versions, a combination of NASA pixel classification algorithm in L2Gen and Idepix was retained.
Cloud Screening The cloud screening implemented here is a combination of the spectral threshold method and classification methods based on feature extraction, including spatial structure  [45, 46] ). It uses properties, such as brightness or whiteness, which are derived from spectral tests, and also abstract properties generated from statistical analysis of the data, for example, by training a neural network. For each property used, an expression is defined to calculate a normalised value within the range 1 (no cloud) to 2 (totally cloudy). The link between the normalised values and the outputs from POLYMER was studied using match-ups in the OC CCI database to identify the range of values for which the error in atmospheric correction was too high. The final classification of a given pixel is an arithmetic combination of the normalised values derived for each property used.
Validation of Pixel-Identification Algorithm A large dataset of manually classified MERIS pixels was collected (PixBox Dataset). A set of attributes was defined, allowing each of the manually selected pixels to be classified by visual inspection. These attributes included totally cloudy; clear sky over water; snow or ice; land; non-clear sky over water; snow or ice; and land. Spatially mixed pixels included cloud over land; partial snow or ice over water; snow or ice over land; and snow or ice over water. Several additional attributes were assigned as appropriate, such as very turbid or floating vegetation. The manual selection was performed by an experienced scientist. The attributes were assigned partly from static background maps or for particular dates, times, and locations. Most attributes, however, were manually assigned by the sampling expert.
The PixBox dataset was taken as the reference against which the automated classification was validated. The unambiguous pixel types are ocean pixels under clear skies, as well as totally cloudy, snow-covered, or ice-covered pixels. The PixBox dataset was constrained to oceanic and coastal waters, i. e. , rivers and inland waters were excluded. The confusion matrix (Table  3 ) shows that the cloud cases were identified correctly by the classification in nearly all cases (15,068 from 15,157 = 99. 4%). However, a significant portion of the clear sky water pixels were misclassified as clouds (1033 from 6468 = 15. 9%), for example, in the coastal zone where bright water can be found. This behaviour is a result of the cloud-screening strategy, which is clear-sky conservative: in doubtful cases, a pixel was removed from further processing to reduce the risk of bad pixels affecting the final products.
In further analyses, many test scenes were processed and the performances of Idepix and L2Gen pixel identifications were compared. If appropriate, a combination of L2Gen and Idepix was used for pixel masking. This procedure, established in the course of developing v1, has been progressively refined in subsequent versions. When implementing pixel masks and flags, one has to weigh the need for high-quality data against the need for high coverage. When creating a climate-quality dataset, the temptation to let suspect data through in the interest of improved coverage has to be resisted. Therefore, in each subsequent version of OC-CCI, we have continued to improve Idepix methodology, to test the outputs, and to compare the results with the L2Gen flags and masks. When in doubt, we have chosen to be conservative and to apply all standard NASA flags to filter L2Gen-processed images rather than to risk contamination of products by poor-quality data.
Additional Filters and Post-Filters In some or all the versions, the well-verified data processor L2Gen packaged as part of SeaDAS was used for processing SeaWiFS, MODIS-Aqua, and VIIRS. Some additional post-filtering was done. Primarily, any pixels with negative R rs values in the 412-560 nm range were discarded, as they would probably have resulted from a flawed atmospheric correction. The 670 nm band was exempted because negatives close to zero are common at this wavelength due to low signal levels; instead, these values were trimmed to zero and the pixels were retained if all other bands were positive. Some other range filters were applied, removing impossibly high reflectances (e. g. , due to unfiltered glint) or chlorophyll well outside the range of case-1 waters that OC-CCI targets.
To express R rs at 510 nm for MODIS-Aqua, band shifting is operated twice from 488 to 510 nm, and from 531 to 510 nm. Then, the final estimated value at 510 nm is the weighted average of the two resultant values. The performance of the QAA appears degraded for long wavelengths, particularly for a ph in the red, whereas it appears reliable at 443 nm  [33, 55] , supporting the choice of that wavelength as a reference. In general, the selection of the QAA for operating the band shifting has the merit of being consistent with the bio-optical algorithm selected here for generating IOPs from satellite data  ([33]  see Section 4. 3). This band-shifting scheme is also used with specific adaptations in the validation activities. The performance of the band-shifting scheme has been assessed using hyper-spectral data from radiative transfer simulations and field observations  [53].
The errors in R rs at the target wavelength are within a few percent, usually well below 5%. Another assessment has been performed with satellite data  [53, 56]. From global products on a one-twelfth-degree resolution grid, all coincident SeaWiFS and MODIS-Aqua daily R rs values were accumulated for the year 2003 and compared (approximately 50 million spectra). After band shifting, the median ratio µ between MODIS-Aqua and SeaWiFS data is closer to 1 in all cases. Thus, µ between MODIS-Aqua R rs at 488 nm and SeaWiFS R rs at 490 nm is 1. 030 versus 1. 007 after band shifting, i. e. , when MODIS-Aqua R rs is expressed at 490 nm. Similarly, µ is 1. 108 for comparisons between MODIS-Aqua data at 547 nm and SeaWiFS R rs at 555 nm, whereas it is 0. 978 after band shifting. At 510 nm, the median ratio is also close to 1 (µ = 0.
These have been shown to vary significantly with time (mainly seasonally), space, and wavelength  [50, 56, [61] [62] [63]. In the operational context discussed here, a simple and robust strategy has been adopted to address the latter two points, i. e. , correcting for multi-year averaged biases computed for each grid point and each R rs band. Specifically, multi-annual averages of R rs expressed at the SeaWiFS wavelengths (i. e. , band-shifted R rs in the case of MODIS-Aqua, MERIS, and VIIRS) are computed over the period of 2003-2007, serving as the reference period when the three missions all functioned in an optimal way without any data gaps. In practice, 4-km daily data are first averaged as monthly composites, which are then combined into a monthly climatology for the period (i. e. , all five January composites combined and so forth).
The mission selected as reference in the context of bias correction is SeaWiFS. The MERIS, MODIS-Aqua, and VIIRS R rs series are thus brought into line with the SeaWiFS record. In the case of VIIRS, since there was no overlap with SeaWIFS, the band shifting was implemented by shifting first to MODIS-Aqua bands as an intermediary step. Therefore, the OC-CCI record for the first five years  (1997) (1998) (1999) (2000) (2001) (2002) , the period for which SeaWiFS is the sole data source, is the SeaWiFS record unaffected by bias correction. Following bias correction, merging is simply averaging the available data for a given pixel (SeaWiFS data and bias-corrected MERIS, MODIS-Aqua, and VIIRS data). Although the number of actual observations within a pixel for each sensor was propagated through the processing chain, it was not used as a weighting factor in merging, as it would have made SeaWiFS contribution small, if not negligible, compared with those of the three other sensors (4-km SeaWiFS GAC would provide one or two observations per 4 km pixel, whereas 1 km MODIS-Aqua or MERIS would provide up to 16).
Instead, the merging used average data for each bin for each sensor, such that each sensor available was given equal weight. In the initial version of OC-CCI, bias correction was applied as a single annual average value. In subsequent versions, the correction was applied using daily, multi-year running averages to eliminate any seasonal components in the bias correction. Albeit simple, bias correction allows the effective removal of the average bias for each grid point and wavelength and guarantees a certain level of consistency for the merged R rs product. As an illustration, Figure  2  shows the average series of R rs at 412 nm accompanied by the number of samples included in the data stream. The first part of the record, when SeaWiFS was the sole data source (up to April 2002), is somewhat noisier with some isolated spikes, whereas the subsequent series is smoother.
This is associated with a manyfold increase in the number of samples per day, as MERIS and MODIS-Aqua start contributing to the merged data set. However, the merged time series appears remarkably stable in time with no artefact associated with the varying availability of each data source, as is required in the context of climate research. Note that we refer here to stability in the data stream when new sensors come online and when old sensors cease to contribute without prejudice regarding any real temporal trends in the data. Since the bias correction is a single inter-sensor correction per location based on a comparison of the 2003-2007 period, the stability of the time series also provides some confidence, indirectly, in the quality of the temporal calibrations applied to each of the missions.
The algorithm comparison demonstrated that all algorithms tested had some desirable features and also that other algorithms sometimes outperformed the selected algorithms in particular tests. Therefore, there is scope for further improvements to the algorithms, possibly by exploiting systems such as the Generalised Inherent Optical Property framework  [69]. When the same data used for development of an algorithm are used also for validation of that algorithm, its performance may appear better relative to another algorithm for which independent data are used for algorithm development and validation. In the comparison carried out, it was difficult to evaluate the impact of independence or non-independence of the in situ dataset used for testing algorithm performance because the development of all algorithms was influenced to a degree by the in situ dataset used here.
For each pixel with match-up data point, the satellite-derived R rs spectrum was analysed to determine the memberships of the different optical classes in that pixel. This allowed us to calculate uncertainties (bias and RMSD) for each optical class, with the fuzzy-logic membership of each class for each match-up observation providing a weighting factor for calculating the mean uncertainty per optical class and for each product. Finally, uncertainties were assigned to each pixel in each image, according to per-class mean uncertainties, with the membership of the classes at the pixel providing a weighting function for the calculation of uncertainties. The process is shown schematically in Figure  3. Further details are provided in Jackson et al. [66]. Each of the products in the ocean-colour ECV product suite (except the backscattering coefficient) has uncertainties (bias and RMSD) assigned to every pixel.
Product Generation A processing chain was then established to generate a time series of ocean-colour data for climate studies, using SeaWiFS, MODIS-Aqua, and MERIS data in the first instance, with VIIRS data added subsequently. Based on user requirements  [20] , it was decided to produce daily products at 4 km resolution. For pragmatic reasons, the processing for different sensors started from different stages: for MODIS-Aqua, when L2Gen was selected for the processing chain (see Table  2  for a list of processors used in different versions of the processing chain), we used data already processed by NASA to level 3 binned, since they were available at the required 4 km resolution; SeaWiFS (GAC and LAC) data were processed from level 2 as they were not available at the 4 km level 3-binned stage; MERIS reduced resolution data (ESA 3rd reprocessing) were processed from level 1B with POLYMER to level 2 and then binned with the BEAM (http://www.
brockmann-consult. de/cms/web/beam/) or SNAP (Sentinels Application Platform, https://www. brockmann-consult. de/portfolio/earthscientific-image-processing/) software. Thus, the processing chain used here is not uniform with respect to the sensors-they joined the chain at different processing stages. From the level 3-binned stage onwards, all processing is unified. Cloud masks were applied to POLYMER level 2 outputs at the binning stage, and a database of in situ measurements was used to generate uncertainty tables. The main steps in the processing chain are illustrated in Figure  5. A major challenge for generating the products was that the components are written in a range of languages and with different characteristics and constraints as they came from many sources and financial and time restraints precluded recoding.
The NetCDF (Network Common Data Form) outputs contain all products and use the NetCDF-4 classic model format, allowing transparent internal compression (critical for products of this volume), and comply with ESA CCI, CF (Climate and Forecast), and Unidata metadata conventions. In addition to the daily NetCDF products, 8-day and monthly composites and specific subsets of products are provided for all versions, as are pre-rendered images. A five-day composite was added from v3. 1 onwards. The variables contained are chlorophyll-a concentration; normalised remote-sensing reflectance (R rs at 412, 443, 490, 510, 555, and 670 nm); vertical attenuation coefficient for downwelling irradiance K d (490); inherent optical properties (a ph , a dg , b bp ) at 412, 443, 490, 510, 555, and 670 nm; a t , the total absorption coefficient, which is included for convenience, though it can be computed from the other components presented and the absorption by pure water itself; optical water classification (8 classes as in  [64]  with an additional class for coccolithophores, which represents the coccolithophore classes identified by  [65]  in v1, but expanded to 14 classes in v2 onwards, see  [66] ); and the number of observations (total and per sensor) at each pixel.
All products have associated per-pixel uncertainties (RMSD and bias), except for a t (convenience product), b bp (insufficient in situ data to make meaningful uncertainty estimates), and the water classes (not applicable). The OC-CCI products are amongst the longest contiguous ocean-colour datasets produced to date. The scaling and robustness issues were addressed by splitting the problem at the natural breakpoint of single days, each encapsulated with all necessary data and logs. In the event of a problem, other containers were unaffected and all relevant debugging information is readily available. This structure lends itself naturally to batch processing on a cluster of commodity nodes. The total processing time is on the order of 2 months with approximately 100 nodes. The primary limitation is I/O demand due in part to the huge data volumes residing on large central file servers and being sent to networked clients for local processing.
Validation of Products The RMSD and bias at the global scale for all paired observations are provided in Table  4  for the products (chlorophyll and R rs ). The results of the product validation have been described in detail in the Uncertainty Characterisation Document (UCD, OC-CCI, 2014c) and in the Product Validation and Intercomparison Report (PVIR, OC-CCI, 2014b). Table  4. Global uncertainty characteristics for oceanic properties derived from OC-CCI merged R rs data for v1 to v4: The results are based on comparison with matched in situ data. Number of match-up observations N that have been used for each of the properties is given. Note that, for chlorophyll-a (Chl-a, in mg m -3 ), the analyses are done on log 10 -transformed data. The units of R rs are sr -1. An important characteristic of the OC-CCI products is the improved temporal and spatial coverage through the data processing approaches adopted, which include the merging of data from multiple missions and the use POLYMER for the atmospheric correction of one or more sensors; POLYMER is capable of retrieving data under sun-glint conditions, which substantially increases the spatial coverage of the data retrieved from MERIS.
However, POLYMER was deemed suitable for processing only a subset of the sensors in each the versions thus far, and so, data gaps due to glint did occur for those sensors which were not processed with POLYMER. It is planned to generate an all-POLYMER version in the future on an experimental basis. The improved coverage has implications for studying phytoplankton dynamics under climate variability and change. For example, phytoplankton phenology can be studied at shorter time scales when coverage is better. As an example, Figure  6  shows the number of days used to create composites for one-degree boxes at two locations in the Atlantic against the fraction of pixels in the composite that had valid data, on average, for OC-CCI-v3. 1 R rs (443) data. For comparison, results from a similar analysis on another merged dataset, GlobColour v02 (http://www.
globcolour. info/), are also shown. If we say, for example, that at least half of the pixels in the area should have data before the composite can be considered representative of the area, then higher coverage would help achieve this target with a smaller number of days. When only single sensors are contributing data (during the SeaWiFS-only period at the beginning of the time series and MODIS-Aqua-only period in v1), then the coverage is poor at times (Figure  7 ), even with 15-day composites. This highlights the importance of having two to three ocean-colour sensors in orbit at the same time if we are to meet the GCOS requirement for daily global data with adequate, representative coverage. We note that, in the context of climate studies, increased coverage should not be achieved at the cost of reduced accuracy because the datasets are designed, among other purposes, for studying small, long-term trends in ocean-colour products in a field where the climate signal may be masked by variability and oscillations in a broad spectrum of smaller time scales.
A noisy and inaccurate product would make it even harder to identify trends. Similarly, Figure  8  shows that, for certain regions such as the Arabian Sea and the Red Sea, the spatial coverage in OC-CCI-v1. 0 data is better compared with earlier single-sensor products because of the inclusion of MERIS data processed by POLYMER. In July, the coverage is particularly poor in the north-west part of the region in the chlorophyll climatologies for SeaWiFS, MODIS-Aqua, and the Coastal Zone Color Scanner (CZCS) from NASA archives compared with OC-CCI coverage. Note that the coverage shown for OC-CCI is for a particular year of the data and not the climatology. The coverage is, in fact, significantly better for all the MERIS years processed with POLYMER, making it possible to study inter-annual variations in the phytoplankton dynamics in the region during particular months that were previously sampled only poorly.
Additional work is needed to improve consistency in algorithms across sensors and to improve the performance of the algorithms in ultra-oligotrophic and eutrophic waters and in optically-complex coastal waters: according to the uncertainty characterisation by optical classes, these are the areas that need further attention. Updates are being made progressively to the optical water classification and the in situ database such that the uncertainty estimates are also being improved (Table  2 ). Incorporation of data from additional sensors has to be considered, to ensure continuity, and to improve spatial coverage, and notable here is the incorporation of VIIRS data in OC-CCI-v3. 1 and the planned inclusion of data from Sentinel-3: the work presented here has highlighted the difficulties of achieving consistency across disparate sensors and the importance of having a consistent, well-calibrated series of ocean-colour satellites with MERIS-like spectral definition, functioning in operational mode.
Statistical analysis. Data of diameters of mycelia on the media for determining the antifungal effect of bio-sulfur, sizes of inoculated citrus leaves showing symptoms, the number of germinated conidia, and hyphal lengths on surfaces of citrus leaves were analyzed with Duncan's multiple range test using SAS program version 9. 0 (SAS Institute Inc. , Cary, NC, USA). Statistical significance was considered at P < 0. 05.
Tangles of hyphae showed a smooth surface on untreated citrus leaves (data not shown). However, on citrus leaves pretreated with bio-sulfur, most conidia were not germinated. Some germinated conidia were shrunk at 1 day after inoculation (Fig. 6B ). At 3 days after inoculation, some conidia were flat and tidy compared to those of untreated one. Also, some hyphae were morphologically deformed and branched (Fig. 6E ). Two days later, morphologically changed conidia and branched hyphae were frequently observed (data not shown), indicating that conidia and hyphae of D. citri were directly affected by bio-sulfur. More than twofold numbers was counted in the hyphal branch on the observed scanning electron microscopy images pretreated with bio-sulfur compared with those of untreated one (data not shown).
1. Non-bee insects are important for pollination, yet few studies have assessed the effectiveness of sampling these taxa using low cost passive techniques, such as coloured vane traps, among different habitat types. 2. This study sampled 192 sites-108 in wooded and 84 in open habitats within an agricultural region of southern Australia. Pairs of blue and yellow vane traps were placed at each site for a period of seven days during the austral spring. 3. Overall, 3114 flies (Diptera) from 19 families and 528 wasps (non-bee and non-formicid Hymenoptera) from 16 families were collected during the study. This sampling was representative of the region, with vane traps equally or more likely to collect as many families from both taxa as those reported on the Atlas of Living Australia (ALA) database for the sampling area.
Database search To determine if vane traps were effective at sampling a representative fly and wasp community within agriculturally dominated landscapes containing both wooded and open habitats, we compared our sampled fauna to families known to occur within the study region. To do this, we generated an area report using the tools dropdown menu in the Atlas of Living Australia (ALA) spatial portal (Atlas of living Australia, 2019; https://spatial. ala. org. au/), to incorporate a comparable area that encapsulated our sampling area (~3,600 km 2 ). We used only spatially valid records (i. e. those where geographic location was provided). We then compared families recorded on the ALA to those sampled within our study. Note that data provided in an area report could conceivably cover a timespan from 1600 -2018 AD, however no survey date was provided 12 for records with the report, so we must assume that most records span a considerable time period (likely 1900 -present), much longer than our two months of sampling conducted in a single season.