dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). Raw sequence reads are available on NCBI SRA under project number PRJNA596510. Transcript counts were normalized to internal standards to account for methodological biases among samples, including library preparation and sequencing, and also normalized to the volume of water filtered for each sample as previously described  (Wilson et al. , 2017). Values above detection limits were further normalized to the total sample expression sum within each genus-level annotation. This step was required in order to compare eddy differential gene expression based on cellular regulation rather than the total number of cells. Transcripts of Pand Fe-stress marker genes (genes with higher expression levels under low P and Fe concentrations, as reviewed by  Stenegren (2020)  and  Snow et al. (2015) ) were extracted from the data set according to functional annotations assigned by comparison to the Kyoto Encyclopedia of Genes and Genomes (P-stress genes: ppK, sqdB, pstS, sphX, phoD, and phnE; Fe-stress genes: feoB, idiA, and isiA).
The light saturation parameter, E k (μmol quanta m -2 s -1 ), is then derived using the maximum growth rate, μ max , reported in Table  S3  in Supporting Information S1 and the initial slope of μ-E curve presented in the literature, α, as 𝐸𝐸𝑘𝑘 = 𝜇𝜇max 𝛼𝛼 + 𝐸𝐸𝑐𝑐 (4) with 𝐴𝐴 𝐴𝐴𝜇𝜇(𝐸𝐸) tending to 1 for E> E k. Finally, Michaelis-Menten kinetics was used to express the change in relative growth rate as a function of PO 4 3and dFe concentration (nmol L -1 ): PO 3- 4 = PO4 3--PO4 3- min PO4 3--PO4 3- min + PO 3- 4 , ( Fe) = (dFe -dFemin) (dFe -dFemin) + Fe (5) with 𝐴𝐴 dFe∕PO 3- 4 min , the minimum concentration required by diazotrophs to grow, and 𝐴𝐴 𝐴𝐴 PO 3- 4 ∕Fe , the half-saturation coefficient, corresponding to the corrected nutrient concentration (nmol L -1 ) at which f µ equals 0. 5. All models were fitted to published culture data (references in Table  S3  in Supporting Information S1) using the nonlinear least-squares R function "nls" and parameter uncertainties, determined by bootstrap using the R function "nlsBoot", were propagated using "predictNLS" from the R package propagate.
Since the regression included a large number of coefficients, accommodating 103 populations in 2017 and 89 populations in 2018, we used a regularized Lasso regression implemented in the "glmnet" R package to penalize non-significant interactions  (Tibshirani et al. , 2012). This approach allowed us to fit the multi-linear regression only based on significant coefficients (instead of performing a standard multi-linear regression and re-fitting the same observations after excluding non-significant interactions to retrieve post-hoc coefficients). P-values of individual grazing rate estimates are shown in Figure  S2b  in Supporting Information S1 and a time-series of diazotrophs and their putative grazers, as well as model fit 95% confidence intervals and R 2 values, is presented in Figure  S3  in Supporting Information S1.
Depth profiles of nifH gene abundances were measured in eddy centers (Figure  3 , Table  1 , Table  S4  in Supporting Information S2) and along a high-resolution sampling transect spanning the two eddies during the 2017 cruise (Figure  4 ). Automated flow cytometry cell counts were measured continuously in the surface layer, and four consecutive samples (∼2 hr sampling time) corresponding to the nearest nifH samples were binned to provide estimates of cell abundances in eddy centers (Table  1 ). Together, the nifH gene-and cell-based datasets indicate that the small diazotrophs UCYN-A and UCYN-B (Crocosphaera) were numerically dominant over large diazotroph taxa in all sampled eddies. During the 2017 cruise, nifH gene abundances of UCYN-A (up to 1. 4 × 10 6 nifH genes L -1 ) and UCYN-B (up to 6.
Diazotrophic prey (indicated by arrows) were imaged inside copepods/nauplii (a), Katodinium (b), Strombidium (c), Amphidinium (d), Prorocentrum (e), Gymnodinium (f), Gyrodinium (h), Uronema (i), and Cochlodinium (j). Scale bars: 10 μm. cyclonic eddies on diazotrophy independent of seasonal changes. Since samples were collected in the vicinity of Station ALOHA, we also leveraged data from the HOT program  (Karl & Church, 2014)  to provide a historical and a seasonal context to our observations. NFRs in both anticyclone centers examined here were anomalously high relative to previous measurements at Station ALOHA. Surface (25 m) rates in these eddies exceeded all previous NFRs from the time-series (2012-2019), with values 3. 7-and 1. 8-fold above the highest previous HOT observations from the months of sampling (July and April, respectively) (Figure  8 ).
Between 2012 and 2019, 13 trackable eddies occupied Station ALOHA during a HOT cruise, yet no NFR measurements from these cruises occurred close to the eddy center (data not shown); hence, it is possible that we observed high NFRs in anticyclones because we specifically targeted the center of mesoscale eddies. In contrast, rates in both cyclones were within the range of previous observations. During the 2017 cruise, the range in depth-integrated NFRs between eddies, sampled 5 days and ∼2° latitude apart, exceeded the range in monthly mean NFRs from the time-series measurements (∼100-400 μmol N m -2 d -1 , Böttjer et al. , 2017). Our results are similar to the studies of  Fong et al. (2008)  and  Wilson et al. (2017) , which both sampled anticyclonic eddies in the NPSG during July and observed anomalously high NFRs (surface rates of 8.
The metatranscriptome data provide additional evidence that PO 4 3-availability helped drive the difference in Crocosphaera growth rates between eddies: all six Crocosphaera genes related to P acquisition and transport   (Böttjer et al. , 2017)  and from 2004 to 2017 for qPCR-derived nifH gene abundances (data set https:// doi. org/10. 5281/zenodo. 4477269). Medians are shown as thick horizontal lines, 25%-75% quantiles as boxes, the minimum and maximum values (up to 1. 5 times the interquartile range) as whiskers, and outliers as black dots. Observations below detection limits are represented by colored ×. Shading is used to differentiate vertical panels. (commonly used as P-stress markers,  Dyhrman & Haley, 2006; Pereira et al. , 2016)  had lower normalized transcript abundances in the 2017 anticyclone, consistent with a reduction in P-limitation relative to the cyclonic eddy (Figure  6 ).
We view the combined datasets presented here and in previous studies of the 2017 eddies as strong evidence that increased PO 4 3-availability resulted in higher Crocosphaera growth rates in the anticyclone. This may ultimately help explain the higher Crocosphaera nifH gene abundances, biomass, and bulk NFRs observed in the anticyclone relative to the cyclone (Table  1 , Figures  3  and 4 ). Nutrient concentrations do not appear to explain the difference in NFRs between the 2018 eddies. Surface nutrient concentrations in the 2018 eddies were higher than in the 2017 eddies and did not differ between the cyclone and anticyclone, possibly due to deep mixing in the 2018 anticyclone prior to the time of sampling. Our ecophysiological model predicted that most diazotrophs in 2018 were limited by low temperature, with the exception of DDAs, which could not be resolved because thermal adaptation for this group has not been tested in cultures.
For example, some diazotrophs, such as Trichodesmium, use dissolved organic P (DOP) in P-depleted environments  (Dyhrman et al. , 2006; Orchard et al. , 2010; White et al. , 2010). Growth of symbiotic diazotrophs can also depend on the bottom-up control factors of their hosts, such as silica for the diatom hosts of Richelia and Calothrix. We did not report concentrations of DOP, silica, or any micronutrients other than dFe in this study due to a lack of data linking growth rates of cultured diazotrophs to the availability of these nutrients. As such, nutrients and micronutrients not assessed in our model could theoretically have driven differences in NFRs. In sum, of the potentially limiting abiotic factors measured, we find the strongest evidence of differential P limitation. While not exhaustive, our combined datasets, along with model results and long-term trends at Station ALOHA  (Barone et al.
Challenges in Generalizing Eddy Effects on Diazotrophs Our finding of high NFRs in anticyclonic eddies agrees with other recent observations of high NFRs in anticyclones from many ocean regions  (Fong et al. , 2008; Holl et al. , 2007; Liu et al. , 2020; Löscher et al. , 2016; Rahav et al. , 2013; Wilson et al. , 2017). However, there have also been reports of high diazotroph abundances associated with cyclonic eddies  (Olson et al. , 2015) , and on a different cruise that sampled a cyclone-anticyclone eddy pair in the NPSG, surface NFRs were significantly higher in the cyclonic eddy than in the anticyclonic eddy (data set https://doi. org/10. 5281/zenodo. 5565560). At Station ALOHA, the correlation between NFR and SLA measured between 2012 and 2019 has remained non-significant (Spearman correlation, r = 0.
Conclusions Here, we report anomalously high NFRs in two anticyclonic eddies in the NPSG. We coupled diverse datasets and ecological models to explore the mechanisms driving this pattern. Our analyses suggest that the 2017 anticyclone modulated bottom-up (via a reduction in PO 4 3-limitation) and top-down (via eddy-specific grazing) control, allowing for the net accumulation of Crocosphaera cells, and ultimately, high NFRs. This mechanism may drive high NFRs in other anticyclonic eddies in the NPSG, where surface PO 4 3-concentrations are significantly higher in anticyclones than in cyclones  (Barone et al. , 2019) , especially during the summer months of peak Crocosphaera abundance. However, no drivers we tested appear to explain the results of our 2018 expedition, for which the high NFR observed in the anticyclonic eddy remains enigmatic.
Data Availability Statement NFRs and diazotroph abundances, measured by ddPCR and automated flow cytometry, along with environmental data and model output from the eddy cruises can be accessed at https://doi. org/10. 5281/zenodo. 7540299. IFCb images and time-series are available at http://ifcb-data. soest. hawaii. edu/MESOSCOPE (2017) and http:// ifcb-data. soest. hawaii. edu/SCOPEIFCB (2018). Metatranscriptome sequences are available on the NCBI SRA under project numbers PRJNA596510 and PRJNA515070. NFRs and diazotroph nifH gene abundances from the HOT program can be accessed under https://doi. org/10. 5281/zenodo. 3718435, https://doi. org/10. 5281/ zenodo. 6341629, and https://doi. org/10. 5281/zenodo. 4477269.
(8) Here, P(R ) is the PSD of the planet spectrum as discussed in Sect. 3. 1. 1, and H(R ) is the transfer function describing the impact of the spectrograph, detector, and data reduction on the observed spectrum. Assuming an ideal Nyquist-sampled spectrograph that acts as an ideal low-pass filter up to the Nyquist frequency set by the spectral resolution R and that the data reduction functions as an ideal high-pass filter, removing all features below R = R min , we obtained the following equation (see Appendix C for details): S/N ≈ 2 √ 2B σ √ R R R min P(R )dR , (9) where we have used M = 2BR for Nyquist-sampled spectra and with B = (λ maxλ min )/λ central as the relative spectral bandwidth. While the true line spread function and detector sampling of the instrument may differ and are strongly dependent on the spectrograph design, this is beyond the scope of this work.
Data and analysis The SINFONI dataset consisted of 24 science frames of the β Pictoris system with exposure times of 60 seconds in four dithering positions. Each spaxel in the datacube covered 0. 0125 by 0. 025 , and the spectra had a spectral resolution of ∼4500. The star was placed outside the FOV in these observations to allow for longer exposures. Furthermore, they were taken in pupil tracking mode to facilitate the application of ADI. We followed the data reduction process of  Hoeijmakers et al. (2018). First, a master spectrum was generated from the 20 brightest spaxels. To obtain a model of the modulation of the stellar spectrum at each spaxel, we divided the signal in each spaxel by this master spectrum and subsequently low-pass filtered it by convolving with a Gaussian kernel with a standard deviation of 0.
Spectral resolution In Sect. 3. 1, we studied the effect of the spectral resolution under idealised circumstances, that is, where the stellar and telluric contributions are perfectly removed. However, a higher spectral resolution may improve our ability to remove these contributions. We therefore studied the effect of spectral resolution on real observations. Before applying the data analysis described in Sect. 4. 1, we degraded the cubes to different spectral resolutions. This was done by convolving with a Gaussian kernel with an FWHM corresponding to that spectral resolution and then downsampling the spectrum for each spaxel. Molecule mapping was then applied to each of these datasets. Since we had a different number of data points for each spectral resolution, the optimal number of PCA components to apply would potentially also be different.
02 bar and 5 bar. We did not allow for temperature inversions and obtained the full P-T profile using cubic spline interpolation from the four points. We used the mock observations of HR8799 e in the H-band from Section 2. 3. Since the data analysis generally results in the loss of the planet continuum, we retrieved the continuum-removed plane-Article number, page 10 of 16 When inferring properties of exoplanet atmospheres, the absolute value of the likelihood is not of interest. Instead, the difference in likelihoods between two models is of interest, with parameters θ and θ + ∆θ: ∆ ln L = ln L(θ) -ln L(θ + ∆θ) ∝ i s i σ 2 i (t i (θ) -t i (θ + ∆θ)). ( 13 ) The obtained constraints on the parameters thus depends on how much the template changes as a function of the targeted parameter. It is not straightforward to derive this analytically as a function of, for example, spectral resolution.
Finally, we parameterised the pressuretemperature (P-T) profile of the planet using four free points logarithmically distributed between 0. 02 bar and 5 bar. We did not allow for temperature inversions and obtained the full P-T profile using cubic spline interpolation from the four points. We used the mock observations of HR8799 e in the H-band from Section 2. 3. Since the data analysis generally results in the loss of the planet continuum, we retrieved the continuum-removed plane-Article number, page 10 of 16 parameters θ and θ + ∆θ: ∆ ln L = ln L(θ) -ln L(θ + ∆θ) ∝ i s i σ 2 i (t i (θ) -t i (θ + ∆θ)). (13) The obtained constraints on the parameters thus depends on how much the template changes as a function of the targeted parameter. It is not straightforward to derive this analytically as a function of, for example, spectral resolution.
Acknowledgements. We thank  Arthur Vigan  for providing us the representative XAO residuals for SPHERE. We also want to thank the referee for suggestions that have resulted in major improvements in this work. R. L. and I. S. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No  694513. This work has been supported by a grant from  Labex OSUG (Investissements d 'avenir  -ANR10 LABX56 ). C. D. is part of  Labex OSUG  ( ANR10 LABX56 ), and C. D. acknowledges support from the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 832428 -Origins. Acknowledgements. We thank  Arthur Vigan  for providing us the representative XAO residuals for SPHERE.
We also want to thank the referee for suggestions that have resulted in major improvements in this work. R. L. and I. S. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No  694513. This work has been supported by a grant from  Labex OSUG   A157 , page  11  of  16 A&A 675 ,  A157 (2023 ) ( Investissements d'avenir   -ANR10 LABX56 ). C. D. is part of  Labex OSUG  ( ANR10 LABX56 ), and C. D. acknowledges support from the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 832428-Origins.
The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorological variables (automatic weather station and an array of radiometers), snow height and water equivalent (snow pole transect and laser scan) and transects of snow interception by the canopy during precipitation events. The data set also includes continuous measurements from the weather station in the forest from March 2018 to June 2022. Complementing the data sets, the repository of the Observatoire des Sciences de l'Univers de Grenoble data center also includes technical information, photographs and a detailed plan of the instrumentation.
Tree easting and northing values in the RGF 93-Lambert 93 projected coordinate system were then derived from the total station coordinates if available or from their polar coordinates relative to a reference pole. Tree altitude values were computed from the airborne laser scanning data (see Sect. 2. 2. 2) by bilinear interpolation of the ground-classified points at the locations of trees. Figure  2  shows a map of inventoried live trees and canopy heights. In the forest stand inside the fence, most of the trees are between 30 and 40 m high (Fig. 3 ) and the total basal area is about 66. 3 m 2 ha -1. It includes 52 fir, 43 spruce and 33 broadleaved trees. Trees measured outside the southern fence included nine spruces, two firs and seven broadleaves. Tree position accuracy is estimated to be better than 10 cm for the trees measured with the total station inside the fenced area and around 50 cm for ad-   2017 ) reported a precision of 0.
Weather station The weather station was installed under rather dense canopy (sky view factor of ∼ 0. 2) during the first campaign and has been maintained since then. Table  2  lists the sensors installed on the station, their specifications and their accuracy according to the manufacturers. The ultrasonic depth gauge measures the snow height. Ten temperature probes buried in the ground are used to estimate the heat conduction flux; 15 min averages of the data measured at 10 s time intervals are recorded by a Campbell Scientific CR3000 data logger. An AXIS M1125-E camera took pictures of the surface around the weather every 3 h during daytime. These images were used to monitor surface and sensor conditions. A Campbell Scientific IR120 infrared sensor was used to measure the surface temperature of a trunk close to the meteorological station (Fig.
1 ). One-minute averages of longwave irradiance measured at 5 s time intervals were recorded by a Campbell Scientific CR1000 data logger. As an example of the use of these measurements, Fig. 6  shows hourly changes in air and soil temperatures and snow depth from December 2018 until June 2019, a winter characterized by deep and sustained snow cover. Snow cover began to build up in late December, reaching a maximum of 90 cm in early February. Melt rates became significant as air temperatures remained consistently above 0 • C through late February. Snow cover disappeared by the end of March, although there were a few snowfalls through May. Snow cover strongly affected soil temperatures to a depth of 60 cm. The disappearance of the main snow cover at the end of March suddenly reversed the temperature gradient in the soil (the deeper soil became colder than the surface).
Conclusions The data sets collected in the Col de Porte coniferous forest will allow research on the effects of the canopy on snow accumulation and ablation processes under different canopy covers. Two intensive field campaigns were conducted during the winters of  2016-2017 and 2017-2018 , and an automatic weather station has been maintained under the canopy since then. Meteorological and snow measurements (automatic weather station, radiometer array, snow pole transect, laser scan, precipitation tanks to estimate snow interception by the canopy) were complemented by canopy observations (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs). Continuous measurements throughout the year at a high temporal frequency (15 min) from the meteorological station allow hydrological and ecological studies related to seasonal changes in micrometeorological and soil conditions.
Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Any reference to specific equipment types or manufacturers is for informational purposes and does not represent a product endorsement. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Special issue statement. This article is part of the special issue "Hydrometeorological data from mountain and alpine research catchments". It is not associated with a conference.
Acknowledgements. We thank  Laurent Bourges  ( CNRS, OSUG ) for his support in managing the data set. Financial support. This project was conducted within grant no. Labex OSUG@2020 ANR10 LABX56 UGA  and with financial support from the  IGE  and the CEN through a collaboration be-tween the  French institutes IGE , CEN and  INRAE  and the universities of Edinburgh and Northumbria in the UK. Marie Dumont  received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( IVORI ; grant no. 949516 ). Review statement. This paper was edited by  James Thornton  and reviewed by two anonymous referees.
, 2021). This is opposite to  Karamperidou et al. (2015) 's results. Using two snapshot simulations of a mid-Holocene climate and pre-industrial conditions, they computed the variance of EP and CP El Niño events as defined by  Takahashi et al. (2011)  and found an increase in the variance of EP El Niño events and no changes in CP El Niño events in the preindustrial run compared with the mid-Holocene. Here, we compare results obtained with the two types of methods using transient simulations of the last 6,000 years with two reference versions of the IPSL model  (Braconnot et al. , 2019) , as well as two historical datasets  (Hirahara et al. , 2014; Huang et al. , 2017). We analyze the reasons for the difference between the results of  Carré et al. (2021)  and  Karamperidou et al. (2015). We do not judge which definition of ENSO flavors is best and note that the choice of methods should depend on the analysis context.
We, therefore, question the way of interpreting the indicators and the exact meaning of the indicators themselves. We also discuss the implication for model-data comparisons and for assessing ENSO flavor changes in a context of mean state shift that might alter ENSO patterns. Section 2 describes the historical datasets and transient simulations, the two methods of El Niño flavors' computation, and the multi-linear regression approach chosen to characterize El Niño pattern shifts. Section 3 presents the different approaches to El Niño flavor shifts over the Holocene and present times, and discusses how to interpret the results of the two methods.