dataesr/scientific-paragraphs-categorization huggingface dataset:
This is the case for the Ganges-Brahmaputra Delta (GBD), the largest delta in the world  (Brown and Nicholls, 2015; Paszkowski et al. , 2021). We present here a coherent synthesis of vertical elevation change, compaction, and subsidence in this region, revisit previously published data, and update this dataset with newly acquired data. Finally, we analyze these datasets together to extract significant information about the temporal and spatial variability of subsidence in the GBD, one of the most densely populated regions of the world.
Acknowledgements We would like to thank the people of Bangladesh who allowed us to install our equipment at their schools, homes and offices and allowed us to repeatedly visit them. This work has been supported over the years by  NSF   INT 99-00487 ,  NSF   EAR 06-36037 ,  NSF   OISE 09-68354 ,  ONR N00014-11-1-0683 ,  NSF  Coastal  SEES 16-00258  and  OCE-1600319 , and BWDB " Long Term Monitoring, Research and Analysis of Bangladesh Coastal Zone ", part of the  Coastal Embankment Improvement Project , Phase-1 (CEIP-1). The manuscript was greatly improved by comments from reviewers, particularly anonymous Reviewer 1. References.
Other Groups Alphaproteobacteria, Gammaproteobacteria and Bacteroidetes represent the majority of isolated bacteria with QS or QQ capacities and are the main contributors of QS genes in marine metagenomic datasets. However, some data show that other ecologically relevant microorganisms may use QS communications, notably members of Actinobacteria, Firmicutes, Cyanobacteria, Archaea and even bacteriophages.
In addition,  Yu et al. (2020)  used deletion mutants to highlight that the strain Croceicoccus naphthovorans PQ-2 (Sphingomonadales, Alphaproteobacteria), isolated from a marine biofilm, could degrade various PAHs and that AHL-based QS regulated several of its PAH-degrading genes. Finally,  Huang et al. (2013)  highlighted the evident overlap between PAH degradation and AHL-related genes in bacteria and supported this hypothesis by mining the NCBI genome database, suggesting that AHL-based regulation could play a pivotal role in the bioremediation of hydrocarbons and PAHs. Much less is known about the impact of non-AHL QS signals on hydrolytic enzymes, which could either exacerbate or mitigate AHL-based regulation. Indeed,  Van Mooy et al. (2012)  showed that the addition of (S)-4,5-dihydroxy-2,3-pentanedione (DPD, which spontaneously transforms into AI-2 in seawater) decreased extracellular phosphatase production by the epibiont of Trichodesmium colonies, while AHL addition increased phosphatase activity.
shibae, as illustrated by  Koppenhöfer and Lang (2020). These authors re-analyzed several transcriptomic datasets and showed that the redox regulators controlling denitrification (among others) also interacts with the strain QS pathways. One luxI-knockout mutant also had significantly overexpressed denitrification genes. In addition,  Romero et al. (2011b)  showed that AHL amendment to the cyanobacterium Anabaena sp. PCCC7120 strongly inhibited its N 2 fixation ability, possibly via the post-transcriptional regulation of nitrogenase activity. The regulation of the bacterially mediated transformation of N compounds by QS pathways has also been suggested in several non-marine studies. QS was notably shown to impact processes such as anaerobic ammonium oxidation, ammonia oxidation and N 2 fixation in soil and wastewater treatment systems (reviewed in  Wang et al.
The authors measured the effect of three QSIs on sulfate reduction, specific growth rates and biofilm formation. QSI addition significantly decreased the specific growth rates, the biofilm formation and sulfate reduction abilities of the two strains, even at subinhibitory concentrations. In addition, SRB can work in concert with sulfide-oxidizing bacteria (SOB) that use reduced S as an electron donor  (Baumgartner et al. , 2006). As such, it has been hypothesized that QS could contribute to this synergy by coordinating their metabolism  (Decho et al. , 2009 (Decho et al. , , 2010;; Montgomery et al. , 2013) , although experimental data are currently lacking. Consequently, QS-mediated S transformations could be especially important in sediments and microbial mats.
Pollara et al. (2021)  reported the dosing of HHQ in marine waters via a method involving an adsorption step on polystyrene resin, but it required the integration of 400-600 L of seawater. Although AI dosing is necessary, it does not allow the identification of the AI-producing organisms. Metagenomic (or metatranscriptomic) techniques offer the possibility of surveying the presence (or expression) of genes involved in QS and QQ. However, the diversity of the genetic machinery encoding these systems remains under characterized and those genes can be difficult to detect in such datasets. This is especially true for AHL synthase and receptor genes, which are highly variable: known luxR and luxI genes only share 18-25% and 28-35% similarity, respectively, despite the relatively conserved structure of AHLs  (Fuqua et al.
FUNDING This work was supported by the  French National program   EC2CO  ( Ecosphère Continentale et Côtière ) and by an  ISblue  ( Interdisciplinary graduate school for the blue planet)  project ( ANR-17-EURE-0015 ), co-funded by a grant from the  French government  under the  program 'Investissements d'Avenir. 'This work was also carried out in conjunction with the  European Marine Biological Resource Centre (EMBRC-ERIC)  and  EMBRC-France. French state funds are managed by the  ANR  within the  Investments of the Future program  under the grant number  ANR-10-INSB-02.
Field data The measurement data of six field studies were used for the calibration and evaluation of the DayCent model (Fig. 1 ). The most important soil and weather characteristics of the field studies are presented in Table  1. Soil properties available at the plot level were used as input for each modeled treatment. Soil hydraulic properties were estimated using pedotransfer functions (see Appendix). Approximately 24,000 daily N 2 O flux observations were included in the present study considering the frequency of sample collection and the number of chambers per treatment in each field study. Some details of the N 2 O flux measurements in the different field studies are presented in Table  2. The studies using chambers meet the set of criteria defined by  Rochette and Eriksen-Hamel (2008)  for quality control of N 2 O flux measurements (e.
, 2008; Gadermaier et al. , 2012; Krauss et al. , 2017). Nitrous oxide fluxes were measured in four treatments based on a combination of two different types of organic fertilizer and two types of tillage, with four replicates  (Krauss et al. , 2017, dataset: doi. org/10. 5281/zenodo. 1566066). The effect of biodynamic preparations was not included in the N 2 O monitoring. The organic fertilization treatments were (i) cattle slurry alone (Slurry) and cattle manure compost plus slurry (Manure compost). The soil tillage treatments were (i) conventional tillage (15-18 cm, inversion) and (ii) reduced tillage (7-10 cm, non-inversion). The amounts of N input in different fertilization treatments were determined by the N content in slurry and manure compost multiplied by application rates  (Gadermaier et al.
, 2012). In the long-term field study in Oensingen, Switzerland, which is part of the Swiss FluxNet (database code CH-Oe2; for details, see  Emmel et al. , 2018) , the ecosystem-scale N 2 O fluxes were measured in 2019 at high temporal resolution (10 Hz) using the eddy-covariance technique  (Maier and Buchmann, 2019). For the present study, the N 2 O fluxes were averaged to daily values. This field study is managed with intensive crop rotations following the Swiss Integrated Pest Management regime, known as IP-SUISSE. Various types of N inputs have been used since the beginning of the field study. Mineral N inputs were mostly ammonium nitrate-based fertilizers. Organic N inputs were slurry, cattle manure and cattle manure compost. Typical conventional soil tillage practices used in Switzerland were applied at this site, including ploughing (chisel and moldboard), cultivation and rolling for seedbed preparation.
Meteorological data Meteorological data for the field studies were obtained from stations located at the field experiments or from nearby stations (i. e. , at distances up to 5 km) for gap-filling. Data from nearby stations in Switzerland (Basel-Benningen, Wynau, and Reckenholz) were available on the IDAWEB portal of the Swiss Federal Office of Meteorology and Climatology (https://gate. meteoswiss. ch/idaweb) and in France (Dijon and Rennes) on the CLIMATIK portal provided by INRAE (https://intranet. inrae. fr/climatik_v2). Simulations for each field study were performed using the DayCent's extra climate driver's mode with six meteorological variables at a daily resolution. The variables were maximum and minimum air temperature, precipitation, solar radiation, relative air humidity, and wind speed.
Assessment of DayCent's performance The model's predictive ability for N 2 O emissions was assessed using a leave-one-out (LOO) cross-evaluation  (Efron and Tibshirani, 1994; Wallach et al. , 2018). The cross-evaluation was based on splitting the six datasets in five "calibration" sites and one "evaluation" site. In this way, the simulations for each site were carried out by averaging the values of calibrated parameters obtained at remaining sites. It means that for a given site the values of the calibrated parameters obtained for this site were excluded from the calculation of the average values of parameters used to test the model's performance. Only the crops for which a calibration was performed at least for one independent site were included in the cross-evaluation. The model calibration at each site was performed by coupling Day-Cent with PEST (Fig.
2 ). PEST is an abbreviation for "Model-Independent Parameter Estimation". It is a statistical tool based on inverse modeling for iterative selection of the best set of parameter values based on best fit, i. e. , minimization of difference between the modeled and observed values  (Doherty, 2020). For the calibration, the PEST code executes DayCent runs several times with variations of the parameters. The parameter estimation is based on a gradient optimization using a Jacobian matrix of sensitivities of model outputs to parameters  (Doherty, 2020). This process is performed by sequentially varying the parameter values in the input files, running DayCent, recording the output values and comparing them with the observed values. The Gauss-Marquardt-Levenberg algorithm is used by PEST to iteratively select the parameter values minimizing the difference between model outputs and observed values.
Long gaps in measured daily fluxes (≥ 4 d) after fertilization or tillage events were not filled to avoid errors in the calibration process related to the calculation of cumulative N 2 O emissions. Therefore, the modeled daily N 2 O fluxes corresponding to the unfilled gaps were also excluded from the modeled cumulative N 2 O emissions used in the calibration process. Thus, for coupling DayCent and PEST for calibration of N cycle parameters at the stage V described above, new selective datasets were created with simulated N 2 O emission data coincident to the available gap-filled data. Additional details of the inverse modeling for calibration of DayCent parameters using the PEST tool are given by  Rafique et al. (2013)  and  Necpálová et al. , (2015 Necpálová et al. , ( , 2018)). By selecting only crop cycles with entire gap-filled management periods for N 2 O flux measurements, we made a comparison of DayCent estimates against EF approaches commonly used in IPCC Tier 1.
Positive bias Fig. 4. Modeled against observed crop yields for six different field studies in Switzerland and France. Model performance was assessed for default parameterization (a), site-specific calibration (b), and leave-one-out cross-evaluation, i. e. , the mean parameter value of all other sites except the one simulated was used (c). Each symbol stands for a harvest event of a specific treatment and site. Different crop types are indicated by different colors. The agreement between modeled and measured data is described by the coefficient of determination (R 2 ), the relative root mean square error (rRMSE), ratio of performance to interquartile distance (RPIQ) and bias. slightly decreased for crop yields (29-23 g C m -2 ) and turned into negative bias for N 2 O emissions (3. 0 to -2. 5 g N ha -1 d -1 ) after sitespecific calibration.
Discussion Based on an extensive set of N 2 O flux observations we identified which parameters of the biogeochemical model DayCent are most critical for improving the prediction of N 2 O emissions from cropland soils  (Figs. 3, 5 and 6). The data set included measurements from six different sites with various crop rotations, soil management and fertilization types. Our results indicate a significant variability in N cycle parameter values for different sites. This was a limiting factor for the improvement of the model's predictive ability by using an average parameterization strategy. On the other hand, we were able to show that, in the LOO crossevaluation, some of the N cycle parameters deviated significantly from the default value, like MaxNitAmt and wfpsdnitadj (Fig. 6 ). This implies that even the application of average values for these parameters helped to enhance the model's performance in the LOO cross-evaluation (Figs.
Acknowledgements This study was supported by the  Swiss Federal Office for the Environment (FOEN ,  LACHSIM  project  -6510355 ). The EFELE field experiment forms part of the  SOERE-PRO , which is a network of long-term experiments dedicated to the study of impacts of organic waste product recycling and it is certified by  ALLENVI ("Alliance Nationale de Recherche pour l'Environnement")  and integrated as a service of the '' Le Programme d'Investissement d'Avenir'' infrastructure AnaEE-France , overseen by the  French National Research Agency  ( ANR-11-INBS-0001 ). Nitrous oxide measurements performed at the Bretenière site were granted by  Burgundy region. We thank the staff of the  INRAE experimental unit  who supported the field management of the cropping systems and the technicians of Agroecology unit who contributed to field measurements or soil characterization.
The N 2 O measurements in the DOK experiment were granted by the  Swiss Federal Office for the Environment (FOEN)  and the  Swiss Federal Office of Agriculture (FOAG). The N 2 O measurements in the Frick experiment were part of the  TILMAN-ORG  project supported by the  Funding Bodies  of the  FP7 ERA-Net  project  CORE Organic II. The N 2 O measurements at Oensingen were supported by the  Swiss National Science Foundation (SNSF) , within the framework of the  National Research Programme "Sustainable Economy : resourcefriendly, future-oriented, innovative" ( NRP 73 ), in the  InnoFarm  project, Grant-N • 407340_172433. We thank  Lukas Hörtnagl  and  Markus Staudinger  from  ETH  for their support in data processing and technical support, respectively. We thank  Luca Nembrini  for the support in the model simulations,  Robing Giger  for the soil sampling and chemical analysis for the Oensingen site, and  Marlies Sommer  for the analyses of hydraulic properties of the same site.
We aimed to describe in detail: (1) how and at what depth the suckling is performed, its duration, and its frequency; (2) the behavioral signatures of suckling events using accelerometry and depth data; and (3) whether the different kinematic features extracted from only two sensors (i. e. , 3-axis accelerometer and depth sensor) among all the available sensors in the CATS cam tags were sufficient for a supervised machine learning algorithm to automatically detect suckling behavior. Supervised machine learning is a type of machine learning in which an algorithm learns from labelled datasets to classify unforeseen data. Such classification technique would open the opportunity to detect suckling events from data collected by tags without video camera such as Acousonde or Dtags or when visual observations are not good enough (e.
Tag specifications We used CATS cam tags to investigate humpback whale suckling behavior. CATS cam tags are small and lightweight (∼500 g) non-invasive animal-borne multi-sensor tags attached via suction cups. They contain six auxiliary sensors (3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer, pressure/depth sensor, temperature sensor, and light sensor), a hydrophone, and an HD video camera (100 • Field of view). A VHF transmitter (ATS F1835B) attached on the tag allows tracking for tag retrieval. The sampling frequency was set at 10 Hz for the magnetometer, gyroscope, depth, and temperature sensors. Accelerometer sampling rate was set at 400 Hz in 2018 and at 800 Hz in 2019. Accelerometer data from 2019 were however always downsampled to 400 Hz in subsequent analyses for a matter of consistency.
Sensor data processing Data from all sensors were downloaded as CSV files and imported into MATLAB (Mathworks) using dedicated scripts (CATS Matlab toolkit, https://github. com/wgough/ CATS-Methods-Materials,  Cade et al. , 2021). Raw accelerometer data were downsampled to obtain a common sampling rate of 10 Hz across all sensors and depth data (in meters) was smoothed with a 0. 5 s running median filter. In all computation described hereafter the output sampling frequency was of 10 Hz. Sensors reading were rotated to match the calf's orientation frame using established methods and raw animal pitch and roll (in degree) were then calculated  (Johnson & Tyack, 2003; Cade et al. , 2016; Tackaberry et al. , 2020; Cade et al. , 2021). To obtain pitch and roll data representing only the body posture and without fluke stroke signal, we low-pass filtered the raw pitch and roll data (0.
5 s running mean filter. In addition, we calculated the Overall Dynamic Body Acceleration (ODBA, in G and converted to meter/second 2 ) as in  Wilson et al. (2006) , the roll rate (in degree/second), and the depth rate (in meter/second) using custom scripts and the Animal Tag toolbox (http://www. animaltags. org). Depth data helped determine various diving or surface activity phases. We defined diving as any submergence to a depth of >10 m  (Stimpert et al. , 2012; Saloma, 2018). Dives were further divided into three phases: descent, bottom, and ascent phase. As individual dives could include stops at various depths, the bottom phase was defined as the segment at >85% of the maximal dive depth for a dive  (Stimpert et al. , 2012). The descent phase was defined as the segment starting at the surface that immediately preceded the bottom phase.
Video data analysis and suckling data extraction We identified and labelled suckling events from the video files using the Behavioral Observation Research Interactive Software (BORIS;  Friard & Gamba, 2016). The corresponding depth data was displayed concurrent with the video. We defined a suckling event as a period during which the tip of the calf's snout continuously touched (>2 s) the mammary slit of the mother and a milk cloud (see video clip in https://www. youtube. com/watch?v=UcyCgiCieFk), even in low density, was observed in the water during the event or upon release of contact  (Tackaberry et al. , 2020). For each suckling event, we calculated its duration and extracted its corresponding activity phase (descent, bottom, ascent, or surface), together with the average depth, depth rate, speed, FSR, ODBA pitch, roll, and roll rate.
Comparison of suckling with non-suckling segments A reference data set for comparison is needed to contrast and identify the behavioral signatures unique to suckling events. To generate such comparison data, we divided our dataset from each deployment into non-overlapping segments of 20 s (duration comparable to suckling events). Then we selected 10 random segments that did not match to any suckling event for each activity phase in which suckling occurred, similar to  Tackaberry et al. (2020). These selected segments, referred as 'non-suckling segments,' were analyzed for the same characteristics as suckling events (see above). To assess if there was a significant difference between suckling events and non-suckling segments, we used a linear mixed-effects models (LMMs) estimated in R using REML (Restricted Maximum Likelihood) that included the suckling status (suckling versus non-suckling) and the activity phase as fixed effects, and individuals as random effect  (Tackaberry et al.
, 2020). In the models, we considered the following response variables: average depth rate, average speed, average FSR, average ODBA, average pitch, absolute average roll, and average roll rate. Absolute value has been used for roll to emphasize any deviation from zero. The model's reference levels (intercept) corresponded to bottom non-suckling (non-suckling segment occurring at the bottom phase of dive). The LMMs were followed by Tukey's post-hoc multiple comparison test using the R package emmeans  (Lenth et al. , 2018). Statistical significance level was set to α = 0. 05.
Data preparation For the supervised machine learning process, as the goal was to identify automatically suckling in non-labelled data, the entire dataset was included in the analysis (i. e. , data with video from tag on to tag off). For each deployment, we split the dataset into 2 s non-overlapping blocks (windows) and then determined which behavioral period each block fell under (suckling or non-suckling period). Two classes were thus considered: (1) 'suckling' and (2) 'non-suckling. ' Each block falling under both suckling and non-suckling period (i. e. , transition to or from a suckling period) was labelled as 'suckling' only if 3/5 of its duration fell under suckling period. Otherwise, it was labelled as 'non-suckling. ' For each block, we computed 43 features: mean, minimum, maximum, variance, skewness and kurtosis for depth, depth rate, speed, ODBA, pitch, roll, and roll rate, and finally mean for FSR.
Identification of the adequate classifier In order to define an appropriate classification model (machine learning model) for associating the suckling status of the calf with the corresponding patterns of different kinematic features, we trained four types of supervised machine learning algorithms -(i) K-Nearest Neighbors (KNN), (ii) Decision tree, (iii) Ensemble classifiers, and (iv) Support Vector Machine (SVM)-using the MATLAB toolbox Statistics and Machine Learning. We repeatedly performed a 60:40 holdout splits on our data (i. e. , 60% of the data as training set and 40% of the data as testing set) while maintaining class ratios (30 runs): the training set was used for training and the classifier's efficiency was evaluated on the remaining unseen data. The holdout split is a commonly used approach for evaluating general model performance in many machine-learning applications (e.