dataesr/scientific-paragraphs-categorization huggingface dataset:
However, our dataset did not allow us to discuss biogeographic patterns at larger scale of these taxa due to the lack of biological data (e. g. , genetic connectivity, dispersal capacity), spatial coverage over seamounts (mainly limited to summit relying on one-two camera transects) and the coarse taxonomic resolution. Comparison of our observations with other faunal datasets sampled in the channel and on the Mozambique/Madagascar margins would allow further exploration of this question. While the regional scale of our study focused on a comparison at the seamount level by considering each seamount as an own ecosystem, it is not excluded that seamounts on the southern part vs. northern part of the Mozambique channel act as a whole ecosystem on themselves respectively, regarding e. g. , their water masses boundaries, organic matter fluxes, species interactions and connectivity  (Watling and Auster, 2021).
Acknowledgements The authors would like to thank the captains, officers, and crews of  RV's L'Antea, L'Atalante, Le Pourquoi Pas ? and the SCAMPI towedcamera team for their contribution and assistance with data acquisition. We also would like to thank the chief scientists and scientific team of the  BIOMAGLO  (https://doi. org/10. 17600/17004000), PTOLEMEE (https://doi. org/10. 17600/ 14000900 ), BATHYMAY (Pol Guennoc; htt ps://doi. org/10. 17600/4200020 ),  PAMELA-MOZ01 , and  PAMELA   MOZ04  ( Gwénaël Jouet  and  Éric Deville ) cruises and the  PAMELA  project leaders ( Jean-François Bourillet ,  Philippe Bourges , and  Jean-Noël Ferry ) (https://doi. org/10. 18142/236). We are grateful to the engineer implied in the project:  Julie Tourolle  for assisting in the analysis of GIS data,  Catherine Borremans  and  Olivier Soubigou  for assisting with the image annotations and GIS development requirement,  Marie Eve-Julie Pernet  for the processing of the fauna samples used to help faunal identification from images,  Christophe Brandily  for helping in the curation and treatment of the CTD data, and  Charline Guerin  for the reprocessing of the DTM resolution (bathymetric data).
The second category evaluates their sensitivity to the size and position of the convective objects. The third category assesses their dependency on the specific characteristics of the dataset in use. Among the indices scrutinized, none fulfill all the desired conditions, and some conditions are only marginally satisfied. Therefore, we developed a new index, called Organization Index based on Distance and Relative Area (OIDRA), as an example of a well-behaving index. The unmet conditions and differences between indices can explain the discord between different organization studies. The results come down to a guideline that will help to advance our description of deep convective organization.
The TOOCAN dataset The statistical comparison between indices needs a dataset of horizontal binary fields that mimic deep convective clouds for which it is possible to compute the convective organization indices. Since the goal is not to study physical processes but the behavior of the indices, any dataset can be used. However, in order to well represent the typical size, occurrence, and disposition of deep convection in the tropics, we have chosen a real satellite dataset with a good spatial and temporal resolution. The Tracking of organized convection algorithm through 3D segmentation (TOOCAN)  (Fiolleau and Roca, 2013)  provides calibrated infra-red (IR) brightness temperatures (T B ) on the entire tropical band, spanning 30N to 30S  (Fiolleau et al. , 2020)  by combining different geostationary satellites.
In the following, we only use the convective system identification, i. e. the deep convective cloud mask of TOOCAN. We do not use the system identifiers which has been obtained by the tracking. Instead, we reconstruct the convective objects using the grid boxes that are identified as convective systems by TOOCAN, and grouping all 8-connected grid boxes. This procedure is implemented with the python framework developed by  van der Walt et al. (2014). Holes in each object are filled to avoid degenerate dispositions. Then, images with less than two objects are rejected. Finally, a total of 76462 images in the period 2012-2016 is considered for this study. Independently, a dataset is generated to compare with the results obtained with the TOOCAN dataset. This dataset is built as images with randomly placed circular objects of different sizes.
The sizes and the number of objects are tuned to match with the ones of the TOOCAN dataset, in order to have a peer-to-peer comparison between the two datasets. The results obtained with the two datasets are similar, meaning that they don't depend on the nature of the objects. The results obtained with the TOOCAN database are shown in the following, while the ones obtained with the newly generated dataset are shown in the supplement material. In order to provide a context for this study, the frequency distribution of the number of objects and their total area is shown in Fig. 1. For both datasets, the frequency of the number of objects decreases, while the total area of the convective systems increases with increasing number of objects. Examples of the analyzed images are given in the following sections.
4  as the distributions of ∆p. The difference ∆p produced by the perturbation of condition (1) has a long tail for I org , L org , COP, and MICA. A long tail means that one single grid box can heavily modify the index value. The other indices are more peaked at zero. For SCAI and MCAI, the ∆p distributions derive mostly from the average number of objects. In the other cases, the enhanced peak at zero occurs because the indices are closely related to the total area of the objects in the domain, and thus adding one single grid box does not affect their value significantly. Under perturbation (2), all the indices peak at zero, proving that merging two close objects does not affect their value much. However, I org , L org , COP, and ABCOP show a significant tail at negative ∆p. The most frequent sign of ∆p is very important because it indicates if the indices generally classify the perturbed dataset as more or less organized.
In most cases, the sign is negative for perturbation (1), meaning that the organization is reduced. The only index that classifies the perturbed dataset as more organized is ABCOP, which is in contrast with the other organization indices. In particular, adding one convective grid box always produces an increase in the value of ABCOP. For perturbation (2), I org , L org and ABCOP predict a decrease of organization, while SCAI, MCAI, COP, ROME, MICA, and OIDRA predict an increase after the perturbation. In order to quantify the average change of the indices values, we report in Table  2 the    indices decreases as the number of objects increases. As a result, each index becomes noise-safe when a sufficient number of objects is present. The index I org is very sensible to noise at low number of objects, and it becomes noise-safe when more than 35 objects are present.
Second category: intrinsic behavior In this section, we study conditions (3) and (4). Condition (3) states that the degree of organization must increase with the proximity of the objects. This condition is the most important one, and no index of organization can miss it because it encapsulates the essence of convective organization. Condition (4) states that the degree of organization must increase when the object sizes are increasing. The sensitivity study associated with this condition presents distinctly the role of the object sizes for each index. The procedure is similar for conditions (3) and (  4 ). Firstly, every image in the dataset has been extended rightward by 50 grid boxes with an empty space. Then an object, called 'test object', is placed in the extension. For condition (3), the test object is as big as 10x10, it is placed in the leftmost part of the extension, and the perturbation consists in moving it rightward up to the end as shown in Fig.
6a. The perturbation is parametrized by the magnitude of the displacement. For condition (4), the test object has a squared shape of 2x2, it is placed in the center of the extension, and the perturbation consists in increasing its size up to 48x48 as shown in Fig. 6b. The perturbation is parametrized by half the length of the object side. The reference configuration is the one where the test object is present and is not perturbed, and all the other configurations are compared to that one. Fig. 7a  and Fig. 7b  show the distribution of ∆p(COP) as a function of the perturbation of both conditions. The two figures show that COP has the correct trends for both perturbations: COP decreases when the test object is moving away, and it increases when the test object size is increasing. In addition, the high correlation with the reference dataset proves that the value of COP is still linked to the reference value after the perturbations.
Third category: Capacity to compare across diverse datasets. Having multiple independent studies that agree with each other is crucial for advancing scientific understanding. It shows that the results are reliable and can be trusted. In the specific case of convective organization, those independent studies can be performed by analyses that use different datasets. Therefore, it is crucial to ensure that the indices of organization do not depend significantly on the dataset characteristics. Otherwise, achieving consensus among different studies becomes exceedingly challenging. Conditions (  5 ), (  6 ), and (  7 ) are meant to evaluate the possible dependency of the indices on the resolution and domain limits. In the following, each of them is discussed separately.
Condition (5): sensitivity to the horizontal spatial resolution The datasets that are used to study convective organization may be different, in particular, the horizontal resolution used in the various analyses may vary up to one order of magnitude. It is thus of great importance to understand the role of horizontal resolution on the organization indices. In this section, we evaluate condition (5) which states that the horizontal resolution of the dataset should not influence significantly the value of the organization indices. The role of the resolution is studied by down-scaling each image of factors 2, 3, 4, 5, and 6. In other words, the images, that are initially 120x120, are reduced to 60x60, 40x40, 30x30, 25x25, and 20x20 grid boxes. The reduction is performed by grouping the initial images' grid boxes in blocks of nxn, then the mean values over the blocks are computed where the values 1 and 0 are associated with convective and non-convective regions.
If the mean value is larger than 0. 5, the block is set to 1 (i. e. it is associated with convection), otherwise, if the mean value is smaller or equal to 0. 5, the block is set to zero (it is not associated with convection). An example is given in Fig. 8 , comparing an image with the original resolution of 120x120 grid boxes and an image with the resolution down-scaled by a factor 3, so that it consists of 40x40 grid boxes. The set of images with the original resolution is considered to be the reference sample, and the ones with different resolutions are the perturbed datasets. When the grid box size is rescaled, the value of each index may change correspondingly, depending on the index units. ROME and the total area of convection have a dimension of km 2 , therefore the area per grid box has to be corrected when the resolution is changed.
SCAI and MCAI are inversely proportional to the number of grid boxes, thus they quadratically increase with the resolution down-scaling. All the other indices are dimensionless and they do not have any explicit dependency on the resolution. These different scaling factors are removed by multiplying the indices by the inverse factor so that, in principle, the average value of the indices should not change. Such an operation is crucial to study differences between the reference and the modified dataset. due to the smaller objects in the domain (the ones with sizes of 1 or 2 grid boxes). The smaller objects can be lost (if they are isolated) or can be merged with bigger objects nearby, and in both cases, the consequences can be significant. These effects are particularly pronounced for I org : the disappearance of the small objects produces an increase in the nearest neighbor distances  The mean of ∆p is shown in Fig.
11a  for different resolutions for the indices. When the resolution scale factor is small, the values of < ∆p > are close to zero, and they move away from zero when the scale factor is increasing. The sign of < ∆p > is different for different indices: it is negative for ABCOP and I org , and it is positive for all other indices. The index which shows less sensitivity to different resolutions on both spread (not shown) and average of ∆p is OIDRA. The most important information for comparing the datasets with different spatial resolutions is given in Fig. 11b. The figure shows the correlations between the reference dataset and the datasets with lower resolutions. They vary with respect to the originally observed values, decreasing monotonically with increasing scaling factor. The total area is not much affected by the change in resolution, and, along with it, the indices more related to the total area are also the ones less sensible to changes in resolution.
Secondly, when organization is linked to other atmospheric variables that are measured with a time delay (e. g. , from a different instrument), the rapid variability of organization may cause the relationship to weaken or disappear due to the time lag. By considering these cases, this analysis helps to account for temporal differences and enables a more accurate understanding of the relationship between organization and other variables in atmospheric studies. is similar, as well as the correlation with the reference dataset. The correlations of the indices between the shifted and the initial domains are shown in Fig. 14. They are large for small shifts and they are slowly decreasing as the domain moves eastward. In particular, for small shifts, the trend is about 0. 01 per grid box, and since a one-grid box shift corresponds to 1% of the image, the correlations are decreasing approximately at a rate of 0.
Conclusions Convective organization is an emerging topic that has been receiving a lot of attention in recent years because of its potential implications for weather and climate. Several indices have been developed to identify organization and they became an essential tool to deepen our knowledge on this topic. In this study, we assess the reliability of each index by employing inno- vative methodology. Initially, we established a set of criteria that are anticipated for convective organization. Subsequently, we evaluated the indices to ascertain whether they met these established conditions. In this article, we compared 9 object-based indices. The conditions are assessed by applying different perturbations to the dataset and measuring the repercussion on the indices. The results are obtained using the TOOCAN systems mask.
The entire analysis is run also using the generated dataset shown in Fig. 1. The results are similar and they are shown in the supplement material. In the previous sections, the indices are compared for each condition. In the following list, we summarize the results for each index, combining the different conditions to obtain the global picture that emerges from the results: -I org : The index I org proves to be inadequate for quantifying convective organization, both in terms of consistency and in terms of comparability. First of all, it is very sensitive to noise. Secondly, when two close objects are merged, I org has large changes, and on average, it predicts the new configuration to be less organized. In particular, the index I org is not noise-safe when the number of objects in the domain is less than 35.
Nonetheless, their correlations with the original resolution decrease slowly, making them suitable indices for comparing different resolutions. Similarly, SCAI and MCAI are the indices that are less affected by shifts in time and in space. -ROME: The index ROME satisfies most of the conditions presented here, however, its biggest problem is the very high correlation with the mean objects' size i A i /N as shown in Table  1. Thus, it is not convenient to quantify convection with ROME (equation A9) when a simpler formula can be used to obtain a very similar value. ROME is noise-safe enough, having values of ∆p smaller than 3. Knowing the correlation with the mean area, we can state that those conditions are satisfied because of the dataset instead of the index behavior. Among all, ROME is the index that depends less on the proximity of the objects, with a variation of only ∆p ≲0.
3 when a 10x10 test object is moving away from the other convective objects. Moreover, ROME depends very much more on the size of the objects. The dataset's horizontal resolution does not strongly affect the ROME index, and the autocorrelation shows a slow decrease with time. The changes of ROME with the considered domain are about 0. 5% for a 1% shift of the domain. -MICA: The index MICA is particular because of its unique definition which has to be kept in mind when reading these results and when using MICA for performing an analysis. The obtained results here are very much influenced by the type of data and the adopted strategy. For example, this work suggests that MICA is greatly influenced by the proximity of objects, however, this is not true in general, and it is true only when the test object is changing A cld (defined in Appendix A0.
Acknowledgements. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Centre National d'Etudes Spatiales (CNES) , and the  TTL-Xing   ANR-17-CE01-0015  project. The authors express their gratitude to  Thomas Fiolleau  for his invaluable assistance in comprehending and utilizing the TOOCAN dataset. The authors extend their appreciation to  Giovanni Biagioli  for engaging in constructive discussions regarding the behavior of the organization indices.
Code and data availability. The TOOCAN dataset is available at the official website https://toocan. ipsl. fr/. The code developed to perform this study is available at https://zenodo. org/record/8287752. The random dataset can be generated with the available code, and the entire analysis can be run using the generated dataset. All the plots that the code is producing are already available at https://web. lmd. jussieu. fr/ gmandorli/Assessment_of_the_object-based_indices_of_convective_organization.
average of the absolute percentile difference < |∆p| > for each index under study. Large values reveal a higher sensitivity to noise, while low values indicate noise-safe indices. The indices ABCOP and OIDRA do not change significantly under the perturbation (1). Under the perturbation (2), the indices MICA and OIDRA do not change significantly, while I org , has large changes. For all indices and for both conditions, ∆p is influenced by the average and distribution of the number of objects of the reference dataset: images with a larger number of objects show less sensitivity to noise. Therefore we report the values of < |∆p| > as a function of the number of objects in the reference dataset in Fig. 5. For both perturbations, the sensitivity of all.
Reconstructions of glacier activity and tree line changes from the nearby Kauner Valley (Figure  1 ) further provide insight on past regional climate variability during the late Holocene  (Nicolussi et al. , 2005; Nicolussi and Patzelt, 2000). Although the Eastern Alps became an important mining district during the Bronze Age  (O'Brien, 2015; Stöllner and Oeggl, 2015) , there is little known on prehistoric mining activity for this area of the Central Alps  (Krause, 1987; Stöllner, 2015). In our study, we use geochemical ICP-MS and portable XRF (pXRF) data to identify periods of metallurgy and to assess the degree and scale of human impact at the junction between the Eastern and Western Alps. Portable XRF can be a time and cost-efficient tool to analyse the chemistry of a wide variety of materials (e.
Computing and calculations The calculations and regression analyses are performed in R version 4. 0. 3. (R Core Team, 2021). The Bayesian age-depth model (ADM) is created using the package rbacon version 2. 5. 3  (Blaauw and Christen, 2011)  with a 95 % confidence interval and the IntCal20 radiocarbon calibration curve  (Reimer et al. , 2020). Boundaries are added based on obvious changes in density and humification. Median ages are given as 'cal BP' (calibrated years before 1950). Historical or certain cited ages are given as CE. The peat accumulation rate represents the bulk thickness of one peat sample and the period of time covered (∆thickness/∆time) in mm*year -1. Metal enrichment factors (Me EF) for Pb, Sb, Cu and Zn and atmospheric mineral accumulation rates (MAR) are calculated using the conservative behaviour of Ti in peat  (Nesbitt and Markovics, 1997) , as done in  Shotyk et al.
Elemental profiles The relevant concentration vs depth profiles (Figure  4 ) and further interpretation are based on the pXRF data calibrated for quantification via ICP-MS using the regressions (Figure  3 ). The pXRF profiles have a higher depth resolution, except for Cu and Sb which were only measured by ICP-MS (Figure  4 ). Peaks in Ti exceeding 400 mg*kg -1 and 600 mg*kg -1 appear at 312. 5 and 200 cm (4140 and 2330 cal BP), respectively, with some minor peaks of 400 mg*kg -1 and 260 mg*kg -1 at 88 cm and 62 cm (1050 and 110 cal BP). Rb resembles the course of Ti, staying mostly below 5 mg*kg -1 except for the five peaks parallel to Ti, indicating stronger terrigenous influx into the mire during these periods. A slight increase in Rb is also recorded in the surface layers. K ranges between 300 and 700 mg*kg -1 , with peaks at 312.
Acknowledgements We would like to thank  Marie-Jo Tavella  ( LEFE ) for her assistance in the clean room laboratory, the  Service ICP-MS of Observatoire Midi-Pyrénées Toulouse  for ICP-MS analyses and  Natalia Piotrowska  ( Gliwice University, POL ) for pre-processing the GdA 14 C-samples. Our thanks also go to  Chuxian Li  for her help in the field,  Mathias Bahns  for solving technical issues, our friendly local contacts  Ernst Partl  and his staff at the  Naturpark Kaunergrat  and the  Bezirkshauptmannschaft Landeck  for permissions. We also thank  Klaus Oeggl  for providing us with detailed pollen information from the  Piller Moor  study by  Hubmann  (1994).
A B S T R A C T Combined sewer overflows (CSO) are used to avoid overloading unitary sewers and wastewater treatment plants. Following the European Council Directive on Urban Wastewater Treatment (UWT), CSO discharges are regulated using guidelines that aim to reduce their ecological impact on aquatic systems. A model CSO, which is part of a long-term experimental field observatory, was modified according to these guidelines and used to evaluate the benefits of compliance through analyses of the bacteriological and chemical states of the receiving intermittent stream. The benthic and hyporheic sediments of similar geomorphic units located upstream and downstream of a monitored CSO outlet were compared before and after changes in CSO regimes. Hydrological, pollutants (Metal Trace Elements, MTE; Polycyclic Aromatic Hydrocarbons, PAH; fecal indicator bacteria, FIB), and tpm-based DNA meta-barcoding datasets resolving the occurrences of >700 bacterial species of nearly 200 genera were studied.