dataesr/scientific-paragraphs-categorization huggingface dataset:
Y. Zhang et al. : Evaluating vegetation-atmosphere coupling in an LSM There remains no direct observation of Ga and Gs at flux sites. De  Kauwe et al. (2017)  developed an empirical method to estimate the two terms. In this method, Ga was estimated as an empirical equation using wind speed and friction velocity  (Thom, 1972) , and Gs (mol m -2 s -1 ) was estimated using the inverted Penman-Monteith equation with measured evapotranspiration (ET, in mol m -2 s -1 ) flux: Gs = Gaγ λET s(R net -G) -(s + γ )λET + GaM a cVPD , ( 10 ) where λ is the latent heat of vaporization (J mol -1 ); VPD (Pa) is the vapor pressure deficit; R net (W m -2 ) is the net radiation flux; G (W m -2 ) is the soil heat flux; M a (kg mol -1 ) is molar mass of air, and c is the heat capacity of air (J kg -1 K -1 ). In this study, Ga, Gs and from the dataset of De Kauwe et al.
Simulation setup and modeled calculation The site simulations with ORCHIDEE are forced with observed meteorology in the FLUXNET 2015 dataset  (Pastorello et al. , 2020). The variables include half-hourly time series of air temperature (K), surface pressure (Pa), specific humidity (kg kg -1 ), northerly and easterly wind speeds (m s -1 ), short-wave downward radiation (W m -2 ), longwave downward radiation (W m -2 ), rainfall (kg m -2 s -1 ) and snowfall (kg m -2 s -1 ). Gaps in the FLUXNET meteorology data are filled following  Vuichard and Papale (2015). The PFT classification of FLUXNET is different from the one used in ORCHIDEE. To let ORCHIDEE simulate LE and the conductances without bias, we used a combination of OR-CHIDEE PFTs to represent the vegetation type at each site (Table  S1 ).
Three simulations are performed at each site (Fig. 1 ). The first simulation named Ctrl uses the default configuration and parameters as used in CMIP6 and TRENDY experiments. The second simulation named Clb_gs uses the same configuration as Ctrl but changes the empirical parameters in Eq. (  2 ). New values for a 1 and b 1 are obtained by constraining the modeled formulation of conductance against a global database of leaf-level observations of stomatal conductance from  Lin et al. (2015)  for different PFTs (see Table  S2  and Fig. S1  in the Supplement). Finally, because the ORCHIDEE model prescribes canopy height for each PFT (Table  S3  in the Supplement), which may cause biases in Ga, we performed a last simulation referred to as Clb_ht. The latter simulation also uses the Ctrl configuration but the default canopy height parameters for each PFT are replaced by the canopy height observed at each site.
In all the simulations, we kept the distance between measurement height and canopy height consistent with the observations, to ensure unbiased estimates of aerodynamic conductance in the model. Because canopy height and measurement height are required in the last simu-lation, in this study, we only used 90 sites out of the flux sites in the FLUXNET 2015 dataset where we found both heights. Although De Kauwe et al. (  2017 ) excluded time steps with precipitation and the subsequent 48 half hours to have the LE mainly contributed by transpiration, referring to Gs as "stomatal conductance" in their paper, we still need to keep in mind that the Gs calculated in this way may also contain contributions from several other processes. It includes the conductance related to bare soil evaporation and the one related to water transport in the leaf boundary layer, in addition to the stomatal conductance integrated over the entire canopy.
Leaf area index data Because leaf area is an important factor affecting both aerodynamic and surface conductance, it is necessary to take leaf area into consideration when explaining the decoupling coefficient. However, instantaneous leaf area information is not available at most of the flux sites. To match the space and time of observation-based , we extracted the leaf area index (LAI) from the 8 d 500 m dataset, MOD15A2H, derived from the space-borne MODIS observations  (Myneni et al. , 2015). This LAI dataset shows good consistency with in situ observations  (Xu et al. , 2018). The LAI for a given date is interpolated by averaging the nearest two high-quality LAI observations from the 8 d time series. For the simulated , we used the LAI from the simulations for analyses to keep consistency between and LAI.
Analyses To be comparable with the observation-based dataset, we first used the same criteria to screen the model outputs as De Kauwe et al. (  2017 ), i. e. , (1) only the 3 most productive months, to account for the different timing of summer in the Northern (June, July, August) and Southern (December, January, February) hemispheres are included in the study. This is to maximize the role of transpiration in versus bare soil evaporation in the growing season. (2) Only daytime data from 08:00 to 16:00 (local solar time) are used. (3) Time steps during precipitation or within 2 d after precipitation are excluded. Because the 30 min is very noisy, to reduce the noise in data, we used the daytime average of and explanatory variables in all later analyses. The decoupling coefficient is affected by multiple factors and the relationships between and different factors are often nonlinear.
Interactions among factors To further understand how the model biases the controls of , we explored the interactions between factors that have significantly different impacts between ORCHIDEE and observations (Figs. 7  and 8 ). The interactions between VPD and Tair are shown in Fig. 7. The observation data show that when the SHAP value is positive (Tair > 25 • ), data with larger VPD have smaller values than those with smaller VPD. In ORCHIDEE simulations, although SHAP values vary differently along the temperature gradient compared with observations, similar interactions between VPD and Tair are also found, i. e. , for a given temperature, when the SHAP value is positive, large VPD values tend to result in smaller. In another words, the dependence of to Tair in hot weather is weakened by a high VPD level.
This weakening of dependence on Tair is due to weakened dependence of Gs on Tair under high VPD conditions (Fig. S3 ). A similar interaction between VPD and LAI is also found in both the observations and ORCHIDEE simulations (Fig. 8 ). The data points with VPD > 3 kPa show SHAP values close to zero, indicating that higher VPD tends to also weaken the dependence of on LAI. ORCHIDEE underestimated the weakening effect of high VPD to the to LAI dependence as the SHAP values under high VPD conditions remain very positive/negative compared with the observation.
However, it needs to be kept in mind that the importance of factors depends on vegetation type. In ecosystems with a small vegetation cover (meaning small Gs), or in ecosystems where Gs has small variability, the importance of wind speed will increase. Apart from the abiotic factors, the biotic factors or vegetation properties also play important roles in controlling. The PFT is found to be the second most important factor affecting after VPD in observation data (Fig. 4 ). In ORCHIDEE simulations, the PFT impact on is weaker but still important, especially for different forest types. The pattern of among PFTs found in this study agree well with De  Kauwe et al. (2017). The influences from PFTs on may be due to various reasons. Besides leaf area and canopy height (investigated in this study), different PFTs often have different canopy structure and leaf traits, leading to differences in Ga and Gs.
Limitations Although the simulations and analyses we performed in this study clearly showed how and why the ORCHIDEE LSM has biases in its estimation of the coupling strength, there are still some questions that need to be answered before we can calibrate the processes underlying these biases. First, the coupling strength is the consequence of multiple processes. In this evaluation of , strict criteria have been used to screen the data to have only time steps with LE mainly contributed by transpiration. The effect of other processes (e. g. , soil evaporation) can potentially affect the coupling strength under some circumstances. For instance, the wetland is also strongly affected by evaporation from open water. An understanding of these processes is also important, and our evaluation cannot draw conclusions on how well ORCHIDEE simulates these processes.
Conclusion In summary, in this study we evaluated the vegetationatmosphere coupling strength, , in the ORCHIDEE LSM using an observation-based dataset at 90 flux sites. We found that short vegetation (grassland and cropland) in ORCHIDEE is too tightly coupled to the atmosphere compared to the observation-based estimates, while the coupling strength of forests is generally well estimated by ORCHIDEE. Nevertheless, biases remain in both modeled Ga and Gs. Calibration of parameters controlling the dependence of the stomatal conductance to VPD reduces the biases of Gs in the ORCHIDEE model to a small extent and improves the estimates in short vegetation. Using a set of random forest models and analyses on SHAP values, we found that vegetation tends to be more decoupled to the atmosphere at low wind speed, high temperature, low VPD and large LAI conditions and in short vegetation.
Acknowledgements. The authors are grateful to the  ORCHIDEE group  for their kind help with the model. The authors are very grateful to the  FLUXNET communities  for their efforts with respect to making the sites and collecting data. The authors also thank  Xiaoni Wang  for providing the ORCHIDEE PFT fraction at each flux site. Financial support. This research has been supported by the  European Commission ,  Horizon 2020 Framework Programme  ( 4C  (grant no. 821003 )) and the  Agence Nationale de la Recherche (CLAND  (grant no. 16-CONV-0003 )). Review statement. This paper was edited by  Jatin Kala  and reviewed by two anonymous referees.
Code availability. The ORCHIDEE model code is available at https://forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/ CodeAvalaibilityPublication/ORCHIDEE_2. 2_gmd_2022 (last access: 14 December 2022, ORCHIDEE Group, 2022). Data availability. The data used in this study are from De Kauwe et al. (  2017 ). The FLUXNET data are obtained at https://fluxnet. org (last access: 14 December 2022) (Pastorello et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-15-9111-2022-supplement.
Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.
They have been progressively deployed throughout the world's oceans (Figure  3 ). The scientific use of this wealth of data became limited by our ability to recognize the imaged The plankton quantitative imaging process; the circled numbers identify four strategies for processing the raw images. High throughput (red numbers) from laboratory-or field-based image acquisition to ecological exploitation is allowed by tight interactions between instruments, computers, and human operators. objects accurately and rapidly enough. Enter machine learning. The techniques are detailed in the next section, but, briefly, several strategies have emerged (as shown in Figure  2 ): ( 1 ) use of the features (size in particular) of all objects, with no classification at all  (Lampitt et al. 1993 ); ( 2 ) manual identification of images by experts  (Dennett et al.
2002) ; ( 3 ) machine classification followed by visual inspection and validation/correction of some taxa  (Garcia-Comas et al. 2011)  or all images  (Biard et al. 2016) ; and ( 4 ) machine classification only  (Irigoien et al. 2009 ). In most cases, marine snow is treated as a single heterogeneous entity, despite its overwhelming numerical dominance, because defining objective categories is difficult (but see  Schroeder et al. 2020). The progress of automated techniques and the development of software to facilitate their use by non-computer scientists enabled the use of imaging data sets for research and ecosystem monitoring purposes. In this article, we take a step back and review the body of work at the intersection of machine learning and quantitative pelagic imaging. In particular, we (a) survey the literature on machine classification of plankton and marine snow images and compare a classic and a modern approach in a realistic case study, (b) examine a few studies to explain how machine learning applied to images has improved our understanding of pelagic ecosystems and highlight the promising use.
Cumulative number of images (millions) Figure 3 Accumulation of images and samples contained in a single repository (EcoTaxa;  Picheral et al. 2017). Image data sets collected with different instruments (the UVP, IFCB, FlowCam, ZooScan, ISIIS, etc. ) have been uploaded by an international community of users from more than 350 organizations. Abbreviations: IFCB, Imaging FlowCytobot; ISIIS, In Situ Ichthyoplankton Imaging System; UVP, Underwater Vision Profiler. of image-derived traits to enable new functional ecology approaches, and (c) propose next steps for the future of plankton classifiers, the coordinated acquisition of massive imaging data sets, and the development of tools for quantitative imaging.
These features are then used by a classifier, just like with classic learning; the classifier here is an artificial neural network. The main difference from classic machine learning is that the feature Accuracy: the proportion of objects in a data set for which identifications are correctly predicted; it should be computed on an independent test set extractor and classifier are learned at the same time, from the training set. Therefore, the model tends toward an optimal combination of features for the classifier, and less domain knowledge is necessary to craft the very best features. This approach has proved hugely successful for a variety of problems. To paint a picture of the history of machine learning for plankton image classification, we conducted a systematic literature review that yielded 174 publications (see Supplemental Appendix 1 and the accompanying Supplemental Data containing the references in a commonly readable bibliographic file format).
However, the same technique applied to a real data set of ∼20,000 images containing seven taxa resulted in only ∼60-70% accuracy, because the proportions of taxa changed between the training set and the realistic test set  (Davis et al. 2004 ). Switching to features based on gray-level co-occurrence matrices and support vector machines as the classifier increased the accuracy, to 70%, but only when compared with the worse model from 2004  (Hu & Davis 2005). Three papers have largely shaped the field, because they are highly cited both by other machine learning papers and by ecology papers using the instruments and techniques they present Distribution of the 138 journal articles within the systematic review of machine learning methods applied to plankton images, according to (x axis) how many times they were cited by papers outside of these 138 articles (a proxy for their overall influence, mostly in the field of plankton ecology) versus (y axis) how many times they were cited by another of the 138 articles (a measure of how influential they are in the specific field of machine learning for plankton images).
Sosik & Olson (2007)  derived a much larger set of features based on image moments, general shape and symmetry, and gray-level co-occurrence matrices as well as specialized approaches aimed at detecting spines radiating from phytoplankton cells. Using a support vector machine classifier trained on 3,300 images in 23 taxa, they achieved 88% accuracy on a test set of 3,330 other images. Overall, in their review,  Benfield et al. (2007)  concluded that 70% and 80% accuracy could be reached on data sets with 20 and 10 taxa, respectively. The following years saw marginal technical improvements and mostly ecological applications of these now mature approaches. The next step in the classification of plankton images was the use of CNNs, publicized by the Kaggle competition of 2015. Many CNNs for plankton images were described in conference papers, which are often difficult to access.
The first journal article was by  Luo et al. (2018) , who used a CNN trained on 42,000 images from the ISIIS that were sorted into 108 taxa. The model was evaluated on 75,000 images, for which the 108 taxa were regrouped into 38 broader ones. The overall accuracy was 87%, which looked excellent, but the precision was only 55% for biological categories, which led the authors to postprocess the results for ecological applications. As explained above, a CNN is essentially a feature extractor combined with a classifier. González et al. (2019)  and  Orenstein & Beijbom (2017)  exploited feature extractors trained on nonplankton images combined with classifiers retrained on plankton data sets and showed that such external deep features can be very effective for classifying plankton over multiple data sets of several thousand images.
In addition, those data sets were rarely made public for other studies to reuse, making comparisons between studies impossible. This is changing, however, with the release of the WHOI-Plankton data set  (Sosik et al. 2021) , PlanktonSet  (Cowen et al. 2015) , and ZooScanNet  (Elineau et al. 2018 ). In addition, all plankton data sets are severely unbalanced: One category dominates and is often made up of detritus or unidentifiable objects. The way this dominant category is handled can completely change the accuracy (the metric most often reported because it is easy to understand) and is a source of discrepancies between studies. For example, the accuracy of a classifier trained and evaluated on data sets with equal proportions of all categories is representative of its raw performance, and hence is a perfectly valid metric for machine learning, but may differ considerably from its accuracy on a real data set, depending on the performance on the dominant class.
Fully Automated Versus Manual Classification To illustrate the difference between classic approaches and CNNs, as well as assess the difference a few points in accuracy make for the ecological interpretation of results, we ran a numerical experiment. A plankton data set collected along a time series was classified manually and then reclassified using both a classic classifier (RF on handcrafted features) and a modern CNN (for details, see Supplemental Appendix 2). The data set is quite typical: 850,000 images in 60 categories, significantly dominated by detritus (350,000 images, i. e. , 41% of the data set). It has been checked by several independent experts and has already been used scientifically, highlighting relevant patterns  (Feuilloley et al. 2021). Therefore, this data set allowed us to investigate whether patterns found in the human-validated series could be detected the same way in series predicted purely through machine learning.
Resolving Plankton Temporal Patterns Imaging systems can be deployed in situ and acquire data at a high rate. This creates opportunities for monitoring, as long as the images can be processed in near real time with sufficient accuracy. For example, an early warning system for blooms of the red-tide-forming dinoflagellate Karenia brevis was set up by combining trihourly samples taken by the IFCB with several one-versus-all support vector machine classifiers, one being focused on K. brevis. The classifier was coupled with a notification system that sent an email message once cell counts exceeded a defined threshold  (Campbell et al. 2013). This system was recently extended to use CNNs and include two other target species  (Henrichs et al. 2021). Over seasonal scales, changes in plankton assemblages are described as ecological successions  (Sommer 2012).
Studies of the multidecadal evolution of plankton communities are rare not only because sampling at that scale is difficult, but also because the subsequent identification effort is tremendous. For example, along a time series in the northwestern Mediterranean Sea, the traditional microscope counts of zooplankton performed on some target groups from 1966 to 1993  (Molinero et al. 2008)  were supplemented by digitalization and classification of whole samples. By combining machine learning classifiers and human curation over 12 more years of data, researchers determined that the changes initially described as a trend were actually periodic oscillations in the whole zooplankton community  (Garcia-Comas et al. 2011 , Vandromme et al. 2011). This data acquisition and identification procedure is still in effect today.
Using Images to Infer Functional Traits Beyond taxonomic identification, images of plankton and marine snow can provide rich morphological information at the individual level. Such data enable trait-based analyses to investigate the functioning of planktonic communities or biogeochemical fluxes of marine snow, where a trait is a characteristic of an individual organism or object that affects its fitness or role in the ecosystem. Size has been termed the master trait and is readily measured individually to infer the dynamics of phytoplankton  (Bolaños et al. 2020) , zooplankton  (Romagnan et al. 2016) , and marine snow  (Guidi et al. 2009). Quantitative imaging has recently been used to assess the definitions of phytoplankton size classes, which are classically based on diagnostic pigment analysis.
Improved Plankton Image Classification Several of the studies described above, including recent ones  (Greer et al. 2015) , used manual classification only, even though supervised classifiers were theoretically available to perform at least part of the work. A likely explanation is that image processing and the coding of pipelines for data management are not what plankton ecologists are trained to do. This highlights the need for tools that give ecologists and taxonomists easy access to machine classifiers, even if those are a few points of accuracy below the state of the art, as long as the user interface abstracts the technicalities and can handle thousands of images quickly. Of course, better classifiers are always welcome, and the future clearly lies in a more widespread use of CNNs. But other, existing advances in computer sciences could be leveraged more systematically for plankton imaging.
2021). Even GenBank, the go-to database for genetic information, contains many inaccurate taxonomic identifications  (Lindsay et al. 2017) , so combinations of imaging and sequencing proposed to alleviate the taxonomic bottleneck  (Riedel et al. 2013 ) are also problematic. Therefore, collaboration with trained taxonomists to build and share gold-standard plankton image data sets appears necessary to fully harness the power of modern machine learning techniques.