dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Understanding how animals will cope with environmental changes is a topical challenge in ecology. Since the Arctic is warming twice as fast as the rest of the world it can be seen as a natural laboratory to anticipate changes occurring at a more global scale. Unfortunately, logistical constraints, including year round access, limit fieldwork studies in this part of the world. In addition, biological responses are complex and integrate environmental constraints which may be logistically difficult to evaluate at remote locations. Our results highlight the importance of pursuing long term monitoring programs in the Arctic to improve dataset length and quality, and gain power to elaborate more complex models  88.
Environmental data. For the summer period, environmental data were dealt with within a 160 × 200 km plot surrounding the colony, which also included little auk at-sea habitats as determined through GPS tracking  26  (Supplementary Fig. S1b ). Monthly SST came from the Multi-scale Ultra-high resolution (MUR) SST analysis from the NASA (v4. 1, global 0. 01° resolution, monthly) and were acquired from http://coastwatch. pfeg. noaa. gov/ erddap/index. html. Monthly wind data came from the Metop/ASCAT data set (0. 25° resolution, monthly, starting in 2007) from CERSAT and were acquired from http://cersat. ifremer. fr/. Daily sea-ice concentration (SIC, percentage of sea surface covered by ice in a given area) came from the Eumetsat OSI SAF and were acquired from http://osisaf. met. no/, using the Global Sea Ice Concentration reprocessing dataset (0.
25° resolution, daily). For wind and SST data, we used monthly values for July and we calculated the mean annual value for the foraging area defined above. For SIC, we calculated a mean annual value from the daily SIC concentration in the area between 15 th July and 15 th August. Overwintering locations of birds from Ukaleqarteq were known from birds equipped with geolocators  90  , and we defined the core wintering area of birds as the 50% kernel area of positions between 1 st November to 28 th February obtained for 94 little auks equipped between 2009 and 2015  101. Yearly winter environmental conditions (wind speed, SST) were calculated as a mean value within the core wintering area from November to February from monthly values of the datasets mentioned above. In addition, we calculated the proportion of days with high winds (mean daily wind speed > 40 km/h) during the same period, using daily wind speeds from the Metop/ ASCAT data set (0.
Acknowledgements This study and long-term monitoring program on little auks have been funded by the  French Polar Institute  (grant  388  to DG and JF), the  National Science Foundation (NSF  Grant  0612504  to  Nina Karnovsky & AMAH) , the  National Geographic Society  ( GEFNE 41-12  grant to JF), the  Belmont Forum  ( TAMANI  project to DG), the  European Commission (Marie Curie IEF and Marie Curie CIG  to JF, Projects  273061  and  631203 ), the  French Arctic Initiative -CNRS  ( PARCS  project) and the  Mission pour l'Interdisciplinarité -CNRS  ( Changements en Sibérie  project). We thank all the field assistants for their great work,  Delphine Bonnet  and  Rumsais Blatrix  for their help/technical assistance for the zooplankton identification, Francis Crenner who kindly provided 4 LULs and  Jean-Yves Barnagaud  for inspiring discussions on the methods.
However, I try to minimize the number of programs used in the submitted version of a book or paper, and keep them short. Nowadays I classify tasks in two categories. For time-and space-intensive computations, I am whittling down my code to a few programs in C, submitted or to be submitted to referees. The programs are or will be available upon request. For small computations, I now use Sage/Python code, almost all of it included in the TeX source of the book. (The code for small computations that take more than a couple of seconds has been submitted together with the programs in C just mentioned. ) It is an easy matter to keep Python code brief and readable. Thanks to SageTeX, input and output are automated, in that the output of Sage/Python code is displayed automatically in the file that TeX outputs.
and obtain Hence, f (x) ≤ 12. In this way, the possibility of human error in copying input/output and updating material is reduced. All code can be run afresh between two compilations of the TeX file; indeed, I run it afresh (and have to run it afresh) after any change to any code in the file. Someone downloading the source code may decide to run the code or not; it is very easy to do so for anyone with Sage installed. It is also very easy to display all code; all that is involved is switching a single flag at the beginning of the source code. There is a question that cannot be avoided, namely, whether it is right to use a large computer-algebra system in a proof. It is in general unsatisfactory to say "By M. ,. " 5  before a claim, as some papers do. Consequently, in the first version of my proof of ternary Goldbach, I used Sage only in an exploratory role, and did all coding in C.
At the same time, Sage is open-source, peerreviewed and highly modular (though some of its larger, older components may not themselves be highly modular or peer-reviewed in quite the same way). Because Sage is modular, we depend only on the correctness of the relatively small parts of it that we use in the proof; in my case, that amounts to Python, basic symbolic algebra and ARB. In part for this reason, and in part for the reasons outlined above (code readability, code organization, reduction of human error in input/output), I decided that the advantages of using Sage/Python for small computations that can be embedded in the TeX code overwhelmed any reasonable misgivings one might have about using Sage in a proof in this particular way.
(4) Now, this is a statement in the theory of real closed fields, which is in fact complete; a proof has to exist. It turns out that there is a freely available program QEPCAD  [36]  implementing an algorithm that proves statements in the theory of real closed fields (by CAD, that is, cylindrical algebraic decomposition  [11] ). Can it deal with (4)? Not on my desktop, and that is not surprising: the computational complexity of CAD is doubly exponential in n, with base proportional to the maximal degree of the polynomials involved. However, thinking a little, it is not hard to eliminate a variable in (4), in that one can show that the maximum of the left side minus the right side has to be attained when either y 1 = y 2 , y i = √ x or y i = x holds for at least one of i = 1, 2. QEPCAD proves the resulting inequality in two variables with ease.
Let us mention a couple of examples of these two kinds. • The Galois-McKay conjecture was formulated by Navarro in  [50]  after many, many days of computing in GAP  [25]. • Also after some long experimental computations with the symmetric groups S n , n ≤ 50, Isaacs, Navarro, and I found a natural McKay correspondence (for the prime 2), which should hold for all symmetric groups, and which was subsequently proved in our joint paper with Olsson  [39]. • An old conjecture in Character Theory states that if a finite group G is rational (that is, all χ ∈ Irr(G) are rational-valued), then so are the Sylow 2-subgroups of G. However, after some long (but directed!) search using the "SmallGroups" databases contained in the computer packages  [25]  and  [8] , Isaacs and Navarro have been able to find two counterexamples of order 2 9 • 3 to this conjecture, see  [38].
The two final strains obtained, one resistant (R) and one susceptible (S) to ISy (Gif n • 1088 and 1089), used in this work, differ mainly in the chromosome 2 on which Rlb is located (2R)  [25]. Candidates loci for this gene were then later identified  [25, 26]. Here, we report data from an experimental evolution approach designed to determine whether the venom composition of L. boulardi can evolve rapidly according to the host strain. Hybrids with new venom allelic compositions were obtained by crossing ISm and ISy individuals and then maintaining the offspring on the (R) vs. (S) D. melanogaster strains for 10 generations. The venom composition of individual wasps was then analyzed in F 2 , F 6 and F 10 using two different approaches. The recently developed global approach is based on one dimensional SDS-polyacrylamide gel electrophoresis (1D SDS-PAGE) coupled with further statistical analysis of the intensity of the protein bands.
These high-resolution pictures (5626 × 3745 pixels; 16 bits; TIFF file) were then analyzed with the Phoretix-1D software (version 11. 5, TotalLab, Newcastle-Upon-Tyne, UK) to extract the intensity profile of each lane. The resulting intensity profiles were semi-automatically analyzed using R functions, allowing identification of a dataset of "reference bands" of known molecular weight. The remaining variation in the total intensity of the lanes was normalized with the combination of parameters, "background removed" and "quantile normalization", and the intensity of the reference bands measured with the variable 'height' (see  [13] ). These normalized intensities are the variables that describe the venom composition. The specific analysis was performed using Western blots and antibodies against previously characterized L.
Digital images of the blot acquired with a cooled CCD camera (Andor iKon-M, Abington-on-thames, UK) were used in the following steps. Data were then recorded as follows for each individual: (i) For LbSPN, the genotype at the lbspn locus was determined by the presence/absence of the codominant LbSPNy (54 kDa) and LbSPNm (45 kDa) bands. (ii) For LbGAP, a strong signal is observed in the venom of ISm or (ISm × ISy) F 1 individuals but not in ISy females. The presence/absence of this dominant band was used to distinguish lbgap homozygotes or lbgap/lbgap y heterozygotes from lbgap y homozygotes. (iii) For LbGAP2, the variation in protein quantity is more continuous. We estimated the normalized LbGAP2 quantity as the ratio between the signal intensity on Western blot and a proxy of the amount of venom obtained from the dissection.
Evolution of the Global Venom Composition To evaluate whether the composition of the venom evolve differently on the two hosts, we performed a MANOVA that tested the interaction between the generation (F 2 , F 6 or F 10 ; as a continuous variable) and the host genotype, based on the intensity of the reference venom protein bands. Since band intensities were not normally distributed, we used the permutational MANOVA implemented in the "vegan" R package (function adonis2;  [45] ) with 5000 permutations nested within replicates. This made it possible to evaluate how the Euclidean distances between the venom compositions of individuals are explained by the marginal effect of (i) the host genotype (resistant vs. susceptible; or more accurately the host strain), (ii) the generation (continuous variable), (iii) the interaction between the host genotype and the generation, and (iv) the 16 experimental populations (8 replicates × 2 host strains) to account for the effect of genetic drift.
Since the interaction between the host and the generation could either result from a differential evolution of the venom composition on each host or from its evolution on one host only, the MANOVA was also performed for the (R) and (S) hosts separately. In these additional MANOVA, the marginal effects tested were the generation and the replicate. To characterize the global evolution of the venom composition according to the host, we performed a linear discriminant analysis (LDA) with the six groups of individuals: the three analyzed generations (F 2 , F 6 and F 10 ) on the two host strains (R) and (S). For this, we used the "ade4" R package  [45]  with the individual venom compositions as continuous variables and the combination of host resistance and generation as a factor. Since LDA does not account for the variation between replicates, they were centered before the analysis and significance was assessed using 5000 permutations nested within replicates.
In short, we computed the expected frequencies of the lbspn y allele (and therefore of lbspn m ) and of the LbGAP phenotype (sum of the frequencies of the lbgap/lbgap and lbgap/lbgap y genotypes) for the F 6 and F 10 generations assuming neutrality and panmixia. They were then compared to the frequencies in the eight replicates for each host strain. The observed difference was evaluated on the set of replicates using a summary statistic whose null distribution was built with the simuPOP simulation software  [46]. This software was used to simulate 20,000 times the evolution of a bi-allelic neutral loci evolving in eight haplodiploid populations of the same initial state and size as those in the experiment. The p-values were obtained by doubling the proportion of.
Funding:  Fanny Cavigliasso  was supported by the  French Ministry of Higher Education and Research. Hugo Mathé-Hubert  was funded by the  "Provence Alpes Côte d'Azur" (PACA)  region and the  Plant Health Department of the National Institute of Research on Agronomy (INRA)  and performed in connection with with Afidol. The project received support from the  "University of Nice Sophia Antipolis " (" Incentives Scientific Credits "  2016 ), the  Plant Health Department of INRA , and the  French Ministry of Higher Education and Research  (Grant No. ANR-09-BLAN-0243-01 ). Work was performed in the context of the " Investments for the Future" LABEX SIGNALIFE  (Grant No. ANR-11-LABX-0028 ).
Habitat Characteristics We characterised the habitat within a 25 m radius of each nestbox in order to estimate the fine-scale habitat structure. We focused on two measures of habitat characteristics that have been shown to impact food resources and predation risk: forest edge versus interior  (Huhta et al. , 1999; Batàry and Bàldi, 2004)  and tree diversity  (Gering and Crist, 2000; Sobek et al. , 2009). First, we determined whether a nest was on the edge of the woodland, based on whether or not it was within 5 m of pasture or bog. Most of the other nests were in woodland interiors. Very few nestboxes were on a solitary tree within a pasture or bog, therefore to achieve similar sample sizes between categories we removed the 7 nestboxes in these habitats from the dataset and leading to two categories: woodland edge and interior.
Breeding Monitoring Nestboxes were checked at least twice a week for breeding activity throughout the breeding season (Mid-March to July). Nests were checked daily when fully built and lined (with feather, hair and/or wool), toward clutch completion (from the 6th egg) and from 11 days after the start of incubation, to collect data on lay date and hatch date with a 1-day precision, and on clutch size and the number of hatchlings. Nestling mass and number of nestlings were determined when broods were 15 days old and the number of fledgings was determined as the number of nestlings minus any chicks found dead in the nest after fledging. In total 471 chicks were weighed at 84 nests. We recorded parental feeding behaviour for 3 h at least once for each nest, between 08:45 a. m. and 04:10 p. m. (mean hour = 10:50 a.
We extracted data from 131 videos of 84 nests as two videos, taken 2 days apart, were recorded on most nests. Non-feeding visits are rare at this stage in the nestling period  (Nur, 1984) ; as such, we assumed that nest visits were feeding visits, except for when we could visibly see parents leaving the nestbox with the food item or entering without any food in its beak (considering that we estimated that non-feeding visits represented on average 1. 1% of the visits for each pair in our dataset). Bird capture was carried out under permits to ASC from the French bird ringing office (CRBPO; program 576) and breeding monitoring under permits from the state of Ariége (Préfecture de l'Ariége, Protection des Populations, no A09-4) and the Région Midi-Pyrenées (DIREN, no 2013-02).
Feeding Behaviour Parameters Analysis To describe the pair's feeding behaviour we assessed how often parents visited the nest, how consistently they visited, to what degree they took turns, and whether they visited the nest together. To do so we estimated the following four parameters using the data extracted from the videos: pair mean inter-visit time interval (IVI), standard deviation of the pair IVI, and indices of nest visit synchrony and alternation by the pair, using the methods described in  Savage et al. (2017)  and  Savage and Ouyang (2019). Pair inter-visit time interval (IVI) is the time between two consecutive entries into the nestbox regardless of which parent entered, which is a measure inversely related to the brood feeding rate. An advantage of using brood IVI over feeding rate is that we can calculate an index of variation in IVI (standard deviation here), which provides added information on the consistency with in which food is delivered to the brood.
The synchrony index of pairs was a measure of how likely the two parents were seen together at the nest, relative to that expected by chance. We estimated the actual synchrony of breeding pairs using cross-correlations of kernel-density estimates calculated for the visits of each parent. We then randomised the inter-visit-intervals of each parent 1,000 times to generate disassociated provisioning data, and calculated the synchrony score for each randomisation to generate a distribution of synchrony scores expected by chance. Using the mean and standard deviation of this distribution we calculated the z-score of the natural data as a measure of whether pairs were more or less synchronous than expected given their particular provisioning rates and inter-visit time intervals. Using this method, positive z-scores indicate that pairs are more synchronous than expected by chance given the observed mean pair inter-visits interval, and vice versa for negative z-scores.
To be clear, a high synchrony score does not necessarily correspond to a very short time interval, but rather a shorter time interval than expected from the distribution of observed visits if parents arrive at random. The alternation (or turn-taking) index of pairs was a measure of how often parents visited following their partner versus sequential visits by the same parent. To quantify whether the observed alternation differed from that expected from parents visiting independently, we used the Wald-Wolfowitz runs test  (Wald and Wolfowitz, 1940) , a non-parametric test for independence of elements in a two-valued data sequence [in this case, visits by either the male (M) or the female (F)]. This test is based on the null hypothesis that elements of the sequence are independent and identically distributed (although not necessarily equally common), and generates a z-score for the observed data, which is significantly positive when there are more alternated visits than expected by chance (e.
Statistical Analyses Statistical analyses were performed in R 3. 4. 1 (R Core Team, 2017) using the lme4 package  (Bates et al. , 2015). In all models explanatory variables were mean-centered to facilitate comparisons between the effect of each variable. We used AIC corrected for the sample size (AICc) to select the most parsimonious model, using the dredge function in the package MuMIn  (Bartoń, 2016). This function uses the all-subset approach in which all possible combinations of fixed variables are run, the random structure is kept constant. Models that are better supported by the data while retaining fewer explanatory variables achieve lower AICc values. All rejected terms were added singly to the most parsimonious models to confirm non-significance, and reported effect sizes are derived from final models.
Statistical Analyses of the Ecological Correlates of Pair Feeding Behaviour The complete dataset included 91 observations from 73 pairs across the entire altitudinal gradient. High diversity habitats were in higher proportion at low altitudes (differences between both diversities = -93. 70 ± SD = 30. 58, p<0. 01). Therefore, to avoid confounding habitat effects with altitudinal effects, we performed the analyses testing the effect of tree diversity and habitat type on feeding behaviour on a restricted dataset, including 57 observations from 48 pairs nesting at low altitude. The analysis examining the effect of altitude and weather were performed on the whole dataset. Missing values led to smaller samples in some cases. We investigated which environmental variables significantly explained each of the pair's feeding behaviour parameters by using four sets of general linear mixed models, one for each model.
No correlation between climate variables and altitude were found, so we used the whole dataset to test the effect of climatic conditions. However, as mean temperature and mean humidity during the feeding video were correlated, we used a principal component analysis to create a combined "weather" variable. The first principal component, our measure of "weather, " accounted for 80. 41% of the total variance in these variables. High values of this meteorological variable correspond to high humidity (loading: 0. 71) and low temperature (loading: -0. 71) and vice versa. This meteorological variable was split into extreme values and mid values (between percentile 15 and 85%) and the analyses were conducted for mid values, to avoid bias due to extreme values. Additional variables were included in the models to account for their separate confounding effect on feeding behaviour traits, or to investigate whether they modified the effect of ecological variables on parental behaviour by including interaction terms.
Ecological Correlates of Pair Inter-Visit Intervals The only significant ecological predictor of inter-visit interval was altitude which had a negative effect (Table  3 ). Pairs at high altitude fed significantly less frequently than pairs at low altitude, with a decrease of 5% for each 100 m (Table  3 , Figure  3A ). Parents fed slightly more frequently in habitats with high tree diversity but this effect was not significant (Table  3 , Figure  3A ). The effects included in the final model are indicated by f. Significant effects are in bold. Sample sizes for the whole dataset are 87 observations, 71 pairs. Sample sizes for the restricted dataset are 54 observations, 47 pairs. Reference habitat type is edges. No ecological effects were correlated with the standard deviation in IVI. Only brood size was significantly correlated with it, with parents that had bigger brood sizes having less variation in IVI (Table  4 , Figure  3B ).
Similarly, while inter-visit intervals decreased with altitude, variance in intervisit interval was not influenced by any ecological factors we The effects included in the final model are indicated by f. Significant effects are in bold. Sample sizes for the whole dataset are 91 observations, 73 pairs. Sample sizes for the restricted dataset are 57 observations, 48 pairs. measured. Finally, variation in patterns of parental care had only limited effects on fitness as patterns of care were unrelated to fledgling success despite more synchronous parents within woodlands producing heavier young and pairs that alternated more had a lower variance in within brood mass. Overall, our results suggest that ecology can indeed influence the amount of parental care provided and also patterns of care visits, and that the interplay between environment and coordination behaviours may impact chick growth, but not the number of fledglings.