dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2001). Reference samples for classification were randomly divided into training and validation samples in a 70:30 ratio. The obtained land cover maps achieved an overall accuracy of 97. 4% and 95. 8%, respectively, for 2013 and 2019. The producer's and user's accuracy for the coniferous forests class, which was further analysed, were 100% and 97. 3% for 2013, and 97. 1% and 96. 1% for 2019. The coniferous forests masque derived from land cover classifications was overlaid to acquire coniferous forests change masque in the next step. The change masque was then further visually inspected for errors, such as errors related to residual cloudiness/shading. The coniferous forest change was then spatially intersected with the stand polygons from the FDB, which enabled us to verify the occurrence of mortality in each of the Scots pine stands indicated in the sanitation felling database.
Stands with sanitation felling caused by drought or pests were assigned as class 1 and stands without fellings were assigned as class 0. In the first variant of the model class 1 was limited to stands in which the area of forest cover loss due to sanitation fillings was larger than 0. 1 ha. The adoption of the solution with a minimum area instead of a minimum volume was justified by the fact that if the same proportion of trees dies in terms of volume or number, the probability of mortality in all stands, regardless of their volume and density, will be the same. In order to test whether the introduction of a minimum sanitation felling area (0. 1 ha) has an effect on the results, we also developed a model in which all stands included in the sanitation felling database were included without minimum area threshold.
The model development was performed using the R package "mgcv". Smoothness parameters were chosen using restricted maximum likelihood selection  (Wood, 2011). The final model contains five most important independent variables: stand age, SI, stand density, aspect, and CWB. We used thin plate regression spline ("tp") as smooth function for four predictors (age, SI, stand density, CWB) and cyclic cubic regression splines ("cc") for terrain aspect. Ten basis functions were used ("k = 10″) for stand age, SI and aspect, and five basis functions ("k = 5″) for stand density and CWB. To interpret the influence of the analysed predictor variables on the probability of stand-level mortality we used the partial effect plots. The partial effect plots were created separately for each predictor (stand age, SI, stand density, aspect, CWB) by performing model predictions with keeping values of other variables at their average value.
3 ). The analysis was performed for models including spatial component (X, Y coordinates). As there were no significant differences and slightly better validation statistics were observed in the case of the unbalanced dataset (Supplementary Table  1 ), the final version of the mortality model was developed on imbalanced data. Model validation was performed using the bootstrap approach with iterations. In each bootstrap iteration, a sample with replacement from the input dataset was taken for model training. Then, prediction for the hold-out set was performed, and the performance metrics were calculated based on hold-out observations. On average, 37% of observations were used for validation at each iteration.
g. Borchert, 2002). While several global reviews have described annual tree-ring studies in the world's tropics  (Rozendaal & Zuidema, 2011; Brienen et al. , 2016; Quesada-Román et al. , 2022; Zuidema et al. , 2022) , with over 220 tree species with annual tree-ring formation reported for the Neotropics (i. e. Central American, the Caribbean and South America) alone  (Schöngart et al. , 2017) , the tropics still remain significantly underrepresented in tree-ring studies. This under-representation is clearly evident from the limited coverage of tree-ring chronologies available (Figure  1 ) in the International Tree-Ring Databank (ITRDB) managed by the World Data Service for Paleoclimatology from the National Oceanic and Atmospheric Administration (NOAA). There are 135 treering studies within the tropics and subtropics (mostly in Central America) published ITRDB database and 19 newly developed tree-ring chronologies from 15 tree species (10 studies) have been reported in this Special Issue.
J o u r n a l P r e -p r o o f Combining various anatomical, densitometric, and chemical data via wood anatomical analyses, X-ray fluorescence (chemical) and X-ray densitometry techniques also proved useful for better visualizing the tree-ring annual boundaries in tree species  (Ortega Rodriguez et al. , 2022). Quintilhan et al. , (2021)  also demonstrated the value of histological cuts and X-ray densitometry for accurate visualization of tree-ring boundaries. X-ray densitometry is less laborious and timeconsuming than long histological cuts of the stem tissue. Density profiles are obtained by scanning a wood sample with an x-ray beam, which is absorbed to different degrees by the wood and transformed into a density value by the sensor. This process produces a continuous precise and accurate output that can be superimposed to QWA series to help identify true tree-ring boundaries.
Hua et al. , (2021)  recently published an updated dataset of calibration curves representative of atmospheric radiocarbon variability in the Northern and Southern Hemispheres from 1950-2019, defined by the extraordinary peak of tropospheric radiocarbon caused by nuclear bomb testing in the mid-20th century. This monthly-resolved compilation includes radiocarbon data from both in-situ air measurements and tree-ring records across the globe and has been used as a reference tool for dating. Several tree-ring studies in South America have benefited from using such radiocarbon analyses as a complementary tool for dating rings from complex neotropical woods. Radiocarbon measurements of tree-ring cellulose have confirmed the calendar dates assigned via traditional dendrochronological approaches  (Andreu-Hayles et al.
Introduction Atmospheric ammonia (NH 3 ) primarily originates from agriculture and related activities. Its presence in the atmosphere leads to a reduction of life quality and to millions of premature deaths via its contribution to particulate matter  (Pozzer et al. , 2017). As one of the main forms of reactive nitrogen  (Galloway et al. , 2021) , NH 3 is also a key element in the global nitrogen cycle with devastating effects on the environment when deposited in excess  (Sutton et al. , 2014). Satellite measurements of NH 3 abundances have in the past decade contributed to our understanding of its global distribution, spatio-temporal variations, emission sources, concentration trends, transport patterns, chemistry and deposition levels. Currently, the two most widely-used satellite datasets are those derived from observations of the Cross-track Infrared Sounder (CrIS)  (Shephard et al.
Mean and standard deviation of the difference between the L2 surface temperature and that retrieved from the network are of the order of 0. 5 K and 1. 5 K for cloud fraction up to 25%. A final series of changes concern the network architecture and training database. In previous versions, separate neural networks were employed for the retrieval over land and ocean. These were trained respectively with Gaussian a priori profiles peaking at the surface, and a larger, more general one, with profiles peaking at different altitudes. However, a careful comparison showed that the more general network performed as good as the network trained specifically for profiles peaking at the surface. For this reason, only one network was trained for version 4, for a priori profiles peaking at altitudes z 0 from 0 to 20 km, with a width σ in the range of 0.
Comparison with an optimal estimation retrieval Given the low bias in ANNI NH 3 v3, it is important to exclude the presence of other biases related to the HRI calculation. Here, we present the results of an independent intercomparison that was conducted between the ANNI v4 retrieval output and that of an optimal estimation approach which relies on spectral fitting. The right panels summarize the comparison by means of histograms of the differences. For the optimal estimation retrieval, the Atmosphit forward and inverse model was used  (Coheur et al. , 2005) , which is the same tool whose forward model is used for the construction of the ANNI training database. The optimal estimation was set https://doi. org/10. 5194/amt-2023-48 Preprint. Discussion started: 26 May 2023 c Author(s) 2023. CC BY 4.
Random and systematic uncertainties In total, we report four types of uncertainty for each observation: random or systematic, and with or without the vertical profile uncertainty included. Reporting random and systematic uncertainties separately, is a generally recommended practice  (Boersma et al. , 2004; Merchant et al. , 2017; Sayer et al. , 2020). Random uncertainties describe errors specific to a single measurement, and assuming a normal distribution, these average out over many repeated measurements. Systematic uncertainties are those that exhibit correlations in time or space, and are thus associated with more than one measurement. This type of errors can lead to biases in the measurement dataset. In ANNI v4, both random σ r X and systematic σ s X uncertainties are calculated using Eq.
We release photometric redshifts, reaching ∼0. 7, for ∼14M galaxies at r ≤ 20 in the 11 500 deg 2 of the SDSS north and south Galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on ugriz stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA, and BOSS surv e ys. Representativ e training sets of ∼370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the o v er-representation of luminous red galaxies. The CNN outputs a redshift classification that offers all the benefits of a wellbehaved PDF, with a width efficiently signalling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias, and rate of catastrophic failures of the median point estimate are of order σ MAD = 0.
The photometric data Our catalogue is drawn from the SDSS data release 16 (DR16,  Ahumada et al. 2020  ). The SDSS is a multi-band imaging and spectroscopic redshift surv e y that was conducted on a dedicated 2. 5m telescope at Apache Point Observatory in New Mexico. It provides photometry in the ugriz passbands o v er ∼11,500 deg 2 of the North and South galactic caps to a limiting magnitude of r = 22. 5. Via the SDSS CasJob web service, we retrieved ∼15. 3M catalogue entries of non-point-like sources ( type = 3) with dereddened petrosian magnitudes r ≤ 20, of which ∼1. 5M have spectroscopic redshifts. Thus the final number of purely photometric sources for which we infer redshifts is ∼13. 8M. A sky map of this data set is shown in Appendix A. Photometric redshifts by  Beck et al. ( 2016 )  (hereafter B16 ) are available for nearly all these sources.
They were computed using a k -nearest neighbour algorithm (kNN,  Csabai et al. 2007  ) with five dimensions (the r -band magnitude and 4 colours: ( ug ), ( gr ), ( ri ), ( iz)). The training data included deep, high-redshift  spectroscopic surv e ys in addition to the SDSS. A 3D error map ( r , gr , ri ) built on the uncertainties measured for spectroscopic galaxies helps to identify insecure estimates based on the position of a galaxy in this grid. The accuracy of these photometric redshifts makes them a reference in machine learning based on photometric measurements. Last but not least, they are still the only ones available for comparison purposes.
The GAMA survey The GAMA surv e y  (Driv er et al. 2009 (Driv er et al. , 2011 ; ; Liske et al. 2015  ) is a joint European-Australian spectroscopic surv e y combining UV to FIR photometric data from several ground-and spacebased programs, including SDSS. The spectroscopy was carried out using the 2dF/AAOmega multi-object spectrograph on the Anglo-Australian Telescope, building on previous spectroscopic surv e ys such as SDSS, the 2dF Galaxy Redshift Surv e y and the Millennium Galaxy Catalogue (MGC). We use the 4 equatorial fields (G02, G09, G12, and G15) available in the data release 3 and 4 (DR4,  Driver et al. 2022 ) , co v ering a total of ∼235 deg 2. The spectroscopy is 98 per cent complete to r = 19. 8  (Liske et al. 2015 ) , except for the G02 field where only the region north of Dec ∼ 6deg was observed to high completeness.
Ar chitectur e The present network is a more complex version of the P19 CNN, intended for a more complex data set. Its architecture is diagrammed in Figs  3  and 4. Fig. F1  in Appendix F lists all the layers with their type, shape, number of parameters and the layer(s) they are connected to upstream. We refer to P19 for a pedagogical description of the role played by the different types of layers. Their fig. 4  can also be compared to our Fig. 3. As in P19 , the input data consist of images of galaxies in the five ugriz bands of the SDSS surv e ys, with the galactic extinction along the line of sight added downstream before the fully connected layers (Section 3. 2 ). The training labels are spectroscopic redshifts. Here too, we choose to handle the redshift estimation task by means of a classification rather than, but aided by, a non-linear regression.
Point estimates Although redshift PDFs may be directly incorporated into Bayesian schemes in certain cosmological studies (e. g. inferring cosmological parameters), point estimates are required for many others (especially if the 'PDFs' are not true PDFs). We consider the following 4 photometric redshift estimators: i/ the weighted mean ( z mean ), ii/ the median value ( z med ) and iii/ the peak value ( z peak ) of the 'PDF', and iv/ the regression output ( z reg ). The metrics we use to assess the accuracy of these point estimates are identical to P19. We define: For the purpose of comparison with P19 , we first train the CNN with the same data set: ∼510k SDSS galaxies at r < 17. 8, the same binning: 180 redshift classes in the range 0 < z < 0. 4 with constant width δz = 0. 4/180, and the same protocol as P19 : the database is split into 5 cross-validation samples, each one used in turn for testing while the remaining 80 per cent, augmented with randomly flipped and rotated images.
0022 at z < 0. 33, increasingly larger at z > 0. 33, adding up to 158 bins). The result is shown as the solid pink line. Sparse sampling may not be the only reason for this issue. The z 0. 3 tail of the SDSS data at r < 17. 8 is entirely populated by red galaxies and these are affected by a colour de generac y at this particular redshift, which distorts their CNN redshift distribution (see Section 5. 1 ). The lower panels of Fig. 5  show the PIT and WBT tests. According to both, the new 'PDFs' are slightly o v erconfident where P19 's were underconfident. Their mean CRPS is 0. 0060, versus 0. 0067 for P19. The performance of the various redshift estimators are shown in Table  2  , with the P19 results in parenthesis. P19 had used z mean as optimal point estimate as it minimizes the bias and the rate of  η( > 0.
SDSS + GAMA + BOSS at r ≤ 20 We expand the training to r = 20 using the deeper spectroscopy (Section 2. 2 ). Randomly splitting the full spectroscopic data for training/validation as was done for the SDSS at r < 17. 8 (e. g. 80 per cent/20 per cent) is not appropriate here, as the sample is not at all representative of the galaxies expected to populate the photometric sample. While SDSS and GAMA are representative sets of galaxies at r ≤ 17. 8 and 17. 8 ≤ r ≤ 19. 8, respecti vely, gi ven their large redshift completeness, the bright SDSS data is o v er-represented compared to the faint GAMA data. Both surv e ys also feature strong local structures in their redshift distributions (Fig. 1  ). Last but not least, the large BOSS sample is o v erpopulated with LRGs at z spec 0. 2, which really are quite rare compared to 'normal' red galaxies at r < 20.
In any case, whether the CNN infers in uncharted territories where machine learning techniques are unable to perform or whether the input data are flawed, the PDF widths clearly signal worthless predictions. We use the threshold introduced in the previous section (equation 1 ) to discard them. We also build a galaxy/star/QSO classifier. This is a CNN similar to the redshift classifier, with the same type of input data, into which we insert, at the output of the last convolution, two successive layers of 96 neurons and a final 3 neuron layer for the triple classification. We train it with 80 000 sources in each class, randomly extracted from the SDSS spectroscopic catalogue (type 3 and 6 objects were included for stars and QSOs). The results are cross-validated 5 times, with 80 per cent of the data used for training and 20 per cent for validation.
The full photometric sample The combined ' clean = 0" + ' clean = 1' photometric sample contains ∼11M sources classified as galaxies with PDF widths below the threshold. We simply refer to them as 'galaxies' in the rest of this work. Fig. 25  shows the PDF width-coloured sky distribution of these galaxies, with the same colour code as in Figs  23  and C2  to highlight the effect of the applied threshold. The cleansing procedure rejects 20 per cent per cent of the initial data ( ∼10 per cent are classified as stars or QSOs, ∼17 per cent hav e PDF widths abo v e the threshold). Ho we ver, it is not drastic enough to w ash aw ay the strip y disparities due to the SDSS observing conditions. Fig. 26  shows the DIP score distribution of the photometric galaxies compared to the scaled test sample.
AC K N OW L E D G E M E N T S This work was granted access to the HPC resources of IDRIS under the allocation 2023- AD010414147  made by  GENCI. It was carried out with the support of the  French National Research Agency (ANR)  as part of the  DEEPDIP  project ( ANR-19-CE31-0023 , ht tp://deepdip. net ), and of the  Programme National Cosmologie et Galaxies (PNCG) of INSU , CNRS  with INP and IN2P3, co-funded by  CEA  and  CNES. This publication makes use of Sloan Digital Sk y Surv e y (SDSS) data. Funding for SDSS-III has been provided by the  Alfred P. Sloan F oundation , the P  articipating Institutions , the  National Science Foundation , and the  U. S. Department of Energy Office of Science. The SDSS-III web site is http:// www. sdss3. org/. SDSS-III is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the  SDSS-III Collaboration  including the  University of Arizona , the  Brazilian Participation Group ,  Brookhaven National Laboratory ,  Carnegie Mellon University, University of Florida , the  French Participation Group , the  German Participation Group ,  Harvard University , the  Instituto de Astrofisica de Canarias , the  Michigan State/Notre Dame/JINA Participation Group ,  Johns Hopkins University ,  Lawrence Berkeley National Laboratory ,  Max Planck Institute for Astrophysics ,  Max Planck Institute for Extraterrestrial Physics, New Mexico State University ,  New York University ,  Ohio State University ,  Pennsylvania State Uni versity, Uni versity of Portsmouth ,  Princeton University , the  Spanish Participation Group ,  University of Tokyo ,  University of Utah ,  Vanderbilt Uni versity, Uni versity of Virginia, Uni versity of Washington , and  Yale University.
In Serbia, a large amount of gas seeps or free gas bubbling in natural springs have been studied, and some sites contain up to 85% of H 2  (Randazzo et al. , 2021). However, no systematic study has been performed so far to inventory and characterize such gases in Albania and Kosova in the frame of H 2 generation. The first step of the study consisted in listing and thoroughly locating all referenced gas occurrences at surface. Then, field acquisitions were realized (free gas, dissolved gas, and water sampling) to complete the existing dataset and provide a first overview of natural hydrogen potential associated with the Dinarides ophiolites.
, 1988; Deville and Prinzhofer, 2016) , leading to the presence of OH -based on reaction  [1]. Then, CH 4 in Ujë-i-Bardhë could be a mix between CH 4 coming from thermogenic and serpentinization processes. The area where serpentinization seems to occur is in the West Vardar zone which is consistent with the presence of ophiolites. However, no potential abiotic CH 4 was recorded in the Kruja zone which is a tectonic zone where thrusts go down to the ophiolites. The serpentinization seems, therefore, ongoing on the massif at rather low depth (<2 km), but more data are needed to know if it is also ongoing at great depth.
Conclusion New gas measurements associated with natural springs in Albania and Kosova were made including H 2 data. Different types of springs were measured: CO 2 -rich, N 2 -rich, and CH 4 -rich. Four springs with H 2 occurrence were analyzed. Three of them contained several hundred ppm of H 2 , and one, 16% of H 2. This latter spring was sampled in the north of Kosova in a location named Vuçe. The main process at the origin of H 2 is likely to be serpentinization, occurring at about a 2-km depth based on H isotopic signatures and assuming normal geothermal gradient. No relation was found with He. With this new discovery, five seeps with more than 10% of H 2 are now referenced in Bosnia and Herzegovina, Serbia, and Kosova. This study completes data at the scale of the Dinarides and shows that H 2 gas seeps are mainly located inside ophiolite massifs and not on major faults in the Kruja zone where the gas could have migrated like it is suggested in the Pyrenees  (Lefeuvre et al.
Acknowledgments The authors thank the program  France Relance  for funding the project that was a collaboration between the  University of Pau (UPPA)  and  45-8  Energy. The authors also thank all the persons who allowed us to have access to sampling and data like  Fidaim Sahiti  and  Ivan Kovacevic  and  Bart van der Haven  for the discussion about the data. The authors finally thank  Isabelle Moretti  for the useful discussions about the project and all the reviewers for their useful comments.
On the one hand, in the field of chemical engineering, knowledge bases have been widely used to support the design of chemical products. Outstanding examples of high-ly developed tools implementing knowledge bases include the virtual process-product design laboratory (VPPD-Lab)  [15]  and the computational tool ProCAPD  [27, 28]. Both of them contain property models, databases and knowledge bases to support the design of formulated chemical products. These tools deal mainly with technical knowledge, not including an organizational approach. On the other hand, as knowledge is recognized as a key organizational resource  [29] , diverse knowledge management systems have been applied to support the decision making process during design. For example, Zhong et al. proposed a knowledge management system based on information of customers and the application of kansei engineering concepts  [30] ; Berao et al.
Implicit and Explicit Knowledge In general terms, knowledge can be classified into two categories. It can be explicit and implicit. The former is based on well-established facts while the latter is based on know-how, experience, and intuition  [35]. Explicit knowledge is available in documents, databases, and computer information systems. It is structured and easy to share. Explicit knowledge is normally clear and accessible, and it is usually well-recognized by the design team. An example of explicit knowledge in chemical product design corresponds to established physicochemical specifications, models, equations, formulas, and wellknown production process fundamentals, among others. Otherwise, implicit knowledge is difficult to acquire, transmit, and be conserved within the design teams. This knowledge is also highly related to the design context because it is the result of the trajectory of both the organization and the individuals.