dataesr/scientific-paragraphs-categorization huggingface dataset:
e. for shallow probe-sensing or for data from boreholes/mines. Table  5  shows examples of quality assessments for generic data. Several observations can be made: 1) the uncertainty code provides information on the mathematical uncertainty only. Heat-flow represents a physical quantity based on measured properties which need to be reported in the database, so the uncertainty score is a parameter that could be provided easily by applying simple mathematics. The natural geological heterogeneities involved in heat-flow determination may easily outweigh the uncertainty based on error propagation, but the U-score allows us to assess the mathematical variance independent from those effects. A good U-score (U1) alone, however, is not a guarantee for a high-standard total heat-flow determination. 2) The M-score evaluates the respective methods applied for gradient and thermal conductivity determination.
The scoring starts from a base value of 1 for each parameter. This equal weighting is justified by the Fourier equation for heat-flow determination and is also considered in the approach of  Richards et al. (2012). While  Richards et al. (2012)  consider the used temperature gradient and the underlying temperature measurement separately, we evaluate the temperature gradient in one step (source type). In our new scheme, however, missing metadata ('=M=' in Table  5 ) results in a conservative maximum penalty for this entry. This shall also motivate authors to provide all necessary metadata to the GHFD. The application of the scoring to probe-sensing gradient data is more differentiated than borehole/mines gradient data. However, the reader should keep in mind that also in the latter eight cases are considered under the term "source type and number of temperature points".
Application to data release 2023 Implementation of the new quality scheme (section 3) requires modifications to the previous database structure and field definitions, demanding also a reassessment of the stored data. With the new quality scheme presented here, additional data or the use of an adopted vocabulary for already existing database fields is necessary. This will be considered in the on-going and future assessment process, including also a further re-assessment of the already revised share of data in the light of the new requirements. About 20% of data (ca. 13,000 data points) of the current data release  (Global Heat Flow Data Assessment Group et al. , 2023)  were revised in terms of the structure and definition defined in  Fuchs et al. , 2021a. The quality scheme can be applied without the new fields and definitions, but without this information the majority of quality assessments would be loaded with extra penalties for missing data yielding an artificial bias of the methodological rating (M-score) towards the lower ratings.
For most of the sub groups of the conductivity or temperature-gradient evaluation, this penalty for missing data amounts to -0. 2. Considering the interplay between the ranges of the individual conductivity and temperature-gradient scores and the total classification scheme for the M-score (thresholds at 0. 75, 0. 5 and 0. 25), such a penalty quickly results in a downgrading by one class. This highlights the value of careful documentation of heat-flow metadata to avoid an unnecessary downgrading. From a small, already complete subset of data (n = 143; borehole/ mine data only, ca. 1% of reassessed data), we can derive impressions on the methodological ratings (Fig. 6 ). Here, only a quarter of data achieves the better M1 and M2 ratings, while nearly half of the entries are rated as M4. More in-depth analysis of the quality distribution and statements on its impact have to wait until the new structure is adopted and considered during the assessment of larger parts of the database (Global Heat Flow Data Assessment project; http://www.
Expected benefits and limitations of the IHFC quality evaluation approach In contrast to former approaches to quality evaluation, the scheme allows us to assess the quality of data from different heat-flow determination settings (borehole/mine and probing) at a glance. The combined score considers different dimensions of quality: numerical uncertainty aspects, the methodological quality of temperature and thermal conductivity determinations needed for heat-flow determination, and the possibility of (overriding) perturbing site-specific effects. This evaluation represents the first comprehensive deterministic approach to describe the quality of heat-flow data based on a welldocumented, comprehensible, and consistent procedure. For the evaluation, the provision of a well maintained database comprising all necessary metadata is mandatory.
To provide unambiguous field entries, a set of pre-defined vocabulary for relevant fields was determined that will also facilitate the collection of new heat-flow data and that can be adapted or extended on demand. Because the current approach is based on an algorithmic scoring system and can be calculated programmatically, quality values can also be recalculated easily if the scheme should evolve without complete re-evaluation of the database. However, the application of the scheme requires an extended set of database fields compared to the former GHFD compilations.
Summary and Outlook The new quality scheme, for the first time, allows combining all three relevant dimensions of heat-flow-data quality (quantified uncertainty, methodological quality, status of overruling effect) in one combined score. It allows a quick comparison of heat flow data and reveals missing data or insufficient documentation at one glance. With the new quality scheme, users can select appropriate heat-flow values according to their specific research needs. The adopted and extended database structure makes it possible to interconnect the GHFD to other digital data resources like map data (continents, geology, ocean region), sample data (IGSNs), library services (DOI), etc. In the future, new data relevant to heat-flow determinations may be generated through the interpretation of spatial exploration data and satellite images (e.
g. spatial data of bottom surface reflections or other temperature raster data). Such data may be linked to the GHFD as an add-on service in a separate database. A restructured version of the existing database, which will contain the first systematic application of the quality scheme presented here will be published in early 2024. The process of data screening and revision of incomplete, wrong or empty data entries will be an ongoing process for more years to come. The suggested penalty scheme used for the evaluation represents the current consensus opinion of a broad cross section of the scientific community. However, the authors acknowledge and encourage ongoing discussion and adaptation of the scheme as experience of its application is gained.
Replicated in-situ (p)],[Corrected in-situ (p)], [Replicated in-situ (T)], [Corrected in-situ (interval top {q_top}, Heatflow interval boƩom {q_boƩom} Interval depth reported? TC data from actual heat-flow locaƟon Thermal conducƟvity locaƟon {tc_locaƟon} TC data from nearby or other locaƟon Measurement type QuesƟon this new IHFC database structure to an existing dataset.
Acknowledgments The  International Heat Flow Commission (IHFC)  initiated the discussion on the revision of the Global Heat Flow Database during the 27th  International Union of Geodesy and Geophysics (IUGG) General Assembly (Montreal, Canada ,  07/2019 ). Right on time for the 60th anniversary of the IHFC at the upcoming 28th  IUGG General Assembly (Berlin, Germany ,  07/2023 ), the new quality scheme and the adopted new structure presented here is a major public result of this initiative. Numerous scientists, many current and former members of the  IHFC , have contributed to the discussion, opening new perspectives and sharing their experiences. We would like to thank namely (in alphabetic order):  Irina Artemieva ,  Vladimír Čermák ,  Christoph Clauser ,  Andrea Förster ,  Valiya Mannathal Hamza ,  Derrick Hasterok ,  Shaopeng Huang ,  Alan Jessop ,  Francis Lucazeau ,  Sukanta Roy , and  Jan Szewczyk.
0[ Replicated in-situ (p)],[Corrected in-situ (p)], [Replicated in-situ (T)], [Corrected in-situ (interval top {q_top}, Heatflow interval boƩom {q_boƩom} Interval depth reported? TC data from actual heat-flow locaƟon Thermal conducƟvity locaƟon {tc_locaƟon} TC data from nearby or other locaƟon Measurement type QuesƟon this new IHFC database structure to an existing dataset.
The average redshift accuracy is within 10 km s -1 for the three types of galaxies. The VI process also helps improve the quality of the DESI data by identifying spurious spectral features introduced by the pipeline. Finally, we show examples of unexpected real astronomical objects, such as Lyα emitters and strong lensing candidates, identified by VI. These results demonstrate the importance and utility of visually inspecting data from incoming and upcoming surveys, especially during their early operation phases.
2023, in preparation)  were designed to cover a wider range of parameter space with longer exposure times than the Main Survey. This SV data set is used to explore and define the final target selections for the DESI Main Survey. To support such analyses, one of the key components of the DESI SV is to construct catalogs of the DESI targets with robust redshift identifications. While an automatic data pipeline  (Guy et al. 2022; S. Bailey et al. 2023, in preparation)  has been developed to reduce the raw data and obtain the best-fit redshifts of spectra, it is crucial to have an additional validation of the data set that enables us to (1) assess the quality of data; (2) assess the precision and accuracy of the redshift measurements from the pipeline; and (3) offer feedback for improving the performance of the data pipeline, especially during the early phase of the survey.
To this end, we have visually inspected more than 15,000 DESI SV spectra and produced visual inspection (VI) catalogs of BGS, LRGs, ELGs, and QSOs observed in the DESI SV program. VI of survey data sets has been demonstrated to be essential during the development of a survey (e. g. ,  Newman et al. 2013; Comparat et al. 2016; Pâris et al. 2018). In this work, we describe the procedure that we adopt for visually inspecting DESI spectra. We make use of the VI catalogs to explore the target selections, and to validate the design of the DESI Main Survey. Given that galaxy and quasar spectra have different properties, in this work we focus on the VI catalogs of galaxies, including BGS, LRGs, and ELGs. The QSO VI catalog and results are described in  Alexander et al. (2022). The structure of the paper is as follows.
The procedure of VI is described in Section 2. The data and the VI catalogs are The quality 4 spectrum has multiple spectral features, including both emission and absorption lines. The quality 3 spectrum shows strong Ca II absorption lines with weak NaD and Mgb absorption lines. Spectra with quality 2 and below do not have spectral features that can be robustly identifiable. The right panels show the DESI Legacy Survey images of the BGS targets. The spectra in gray, black, and orange are the originally observed galaxy spectrum, the smooth spectrum with a Gaussian filter, and the error spectrum, respectively. The side length of the image is 18″ and the white circle in the top panel image reflects the DESI fiber size. described in Section 3. We show our analysis for validating the DESI design in Section 4 and demonstrate how this VI process assists the development of the DESI pipeline in Section 5.
Validating the DESI Survey Design with VI Catalogs We use the VI galaxy catalogs obtained from DESI spectra with long exposure times to validate the performance of the algorithm, redrock (S. Bailey et al. 2023, in preparation) , which DESI adopts for recovering the redshifts of galaxies at the designed nominal exposure times, i. e. , effective exposure time ∼1000 s for ELGs  (Raichoor et al. 2022)  and LRGs  (Zhou et al. 2022)  and ∼180 s for bright galaxies  (Hahn et al. 2022). For those galaxies with deep co-added spectra, we use all the data from individual exposures to produce several spectra with similar exposure times to the designed exposure time of the Main Survey. For ELGs and LRGs, we use spectra with 800 < T eff < 1200 s and for BGS, we use spectra with 160 < T eff < 200 s. In this way, one galaxy target has multiple redshift measurements from the redrock algorithm, and we can then compare the redshift measurements from the pipeline with the visually inspected redshift to assess the performance of the DESI observation and data pipeline.
T. W. L. is supported by the  Ministry of Science and Technology  ( MOST 111-2112-M-002-015-MY3 ), the  Ministry of Education, Taiwan  ( MOE Yushan Young Scholar grant  Nos. NTU-110VV007  and  NTU-110VV007-2 ),  National Taiwan University  research grant Nos. NTU-CC-111L894806 ,  NTU-111L7318 , and  NTU-112L7302  and  NSF  grant No. AST-1911140. D. M. A. acknowledges the  Science Technology and Facilities Council  ( STFC ) for support through grant No. ST/ T000244/1. This research is supported by the Director,  Office of Science, Office of High Energy Physics of the U. S. Department of Energy  under contract No. DE-AC02-05CH11231 , and by the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  under the same contract; additional support for DESI is provided by the  U.
Determining how sibling interactions alter the fitness outcomes of dispersal is pivotal for the understanding of family living, but such studies are currently scarce. Using a large demographic dataset on pre-industrial humans from Finland, we studied dispersal consequences on different indicators of lifetime reproductive success according to sex-specific birth rank (a strong determinant of dispersal in our population). Contrary to the predictions of the leading hypotheses, we found no support for differential fitness benefits of dispersal for either males or females undergoing low vs. high sibling competition. Our results are inconsistent with both hypotheses that family members could have different fitness maximizing strategies depending on birth rank, and that dispersal could be mainly driven by indirect fitness benefits for philopatric family members.
maturity, and dispersal patterns strongly depend on sibling interactions  23, 24. These patterns are often driven by differences in access to family resources (e. g. linked with inheritance system or parental favouritism) according to birth order, sex or total number of siblings  23, [25] [26] [27]. Moreover, consistent behavioural differences between siblings according to birth order have been highlighted  28, 29  , thereby suggesting that siblings could have different fitness maximizing strategies. Given that datasets covering family structures throughout time with detailed records on dispersal behaviour are now available in humans, they offer a good opportunity to investigate whether fitness outcomes of dispersal differ between siblings. However, previous studies have focused mainly on dispersal determinants but did not investigate the fitness consequences of dispersal nor included any information on intra-familial competition over family resources  30, 31.
In this study, we used a large demographic dataset from preindustrial Finland to test whether the fitness outcomes of dispersal versus philopatry depend on the level of intra-familial competition. This dataset provides a high follow-up success of both philopatric and dispersing siblings, with complete record of the family structure. Moreover, the effect of sibling interactions on dispersal patterns and fitness outcomes in this population has been separately documented: the number of co-resident same-sex elder siblings is linked to a higher dispersal propensity  26  and a lower lifetime reproductive success  24. Among males, this effect was mainly driven by a competition over inheritable land resources with the eldest son inheriting most of parental resources and subsequently having an increased marriage probability, reproductive success and lower dispersal rates  24, 26.
Discussion Determining the fitness consequences of dispersal and its interplay with sibling interactions is important for understanding the evolution of dispersal and family living and could shed light both on dispersal drivers and on variation of fitness maximizing strategies between family members. Using a large demographic dataset from pre-industrial Finland, we tested whether the outcomes of dispersal decisions on reproductive success depended on an individual's intra-sex birth rank. Overall, our findings did not strongly support this hypothesis (Table  5 ). Instead, the fitness outcomes of dispersal status were similar regardless of an individual's number of same-sex   S4 ), (B) offspring number (N = 3105) (Table  S5 ), (C) offspring survival to age 15 (N = 3061) (Table  S6 ); and for females (D) probability of reproducing (N = 4529) (Table  S4 ), (E) offspring number (N = 3533) (Table  S5 ), (F) offspring survival to age 15 (N = 4491) (Table  S6 ).
Methods Study population. The demographic Finnish dataset used in this study was compiled from records of the Lutheran church which was obliged by law to document all births, marriages, deaths and movements between parishes in the whole country since 1749  55, 56. These data were digitized by the Genealogical Society of Finland HisKi project, and are available at: https:// hiski. genea logia. fi/ histo ria/ indexe. htm. All methods were performed in accordance with the relevant guidelines and regulations. The study period was limited to individuals born before 1900, i. e. before the spread of industrialism  57  , the transition to reduced birth and mortality rates  58, 59  , changes in kin networks  60  and development of the Finnish railway  61. These populations studied mainly depended on farming for their livelihood and were supplemented with fishing in the coastal areas.
We excluded twins as their presence is associated with different family dynamics  68. Dispersal was defined as departure from the birth parish as this information was systematically recorded in the parish registers at the time. Intra-parish movements were considered as non-dispersal, as this information was rarely recorded. We limited the study to the first movement of an individual out of his/her birth parish. Individuals dispersing during childhood (before age 15) as family members were excluded from the data set (< 1% of the overall sample). Using the geographical coordinates of the parishes, we calculated the distances of dispersal for individuals staying in Finland (i. e. not those going abroad, < 1% of the overall sample). As the geographical size of the parishes varied, considering only dispersal from natal parish during lifetime might overestimate dispersal in small parishes and underestimate it in larger parishes.
To take into account the overdispersion of the data, we analysed lifetime fecundity with a negative binomial error structure and a logarithm link function. Finally, we analysed offspring survival to adulthood by fitting the number of surviving offspring as a response term with a logit link function and a binomial denominator equal to lifetime fecundity. All statistical analyses were conducted in R 3. 0. 3 70 using generalized linear mixed effects models (GLMMs) (packages lme4 for binomial error structure and glmmADMB for negative binomial error structure)  71, 72. Multi-model selection and model averaging techniques. We used AIC model selection techniques (R package MuMIn)  73  and an a priori model set corresponding to the different interactions which could have an effect on fitness outcomes (namely the interactions between same-sex elder siblings and lifetime dispersal status, and between same-sex elder siblings and family SES) (detailed below).
The final dataset contained 1282 males and 1083 females. Similarly to previous analyses, we fitted GLMMs on outcomes of reproductive success (probability of reproducing, lifetime fecundity and offspring survival to adulthood). Previous models on dispersal predicted that the most important determinant was the number of philopatric kin and not the number of dispersing ones  16, 49, 77. Therefore, we included the variable "number of philopatric same-sex younger siblings" instead of the number of dispersing same-sex younger siblings. We also included a 4-level categorical variable (0-25%, 25-50%, 50-75% and 75-100%) on the proportion of same-sex younger siblings dispersing during their lifetime, in order to control for the total number of samesex younger siblings. The control variables were the total number of siblings and the family SES.
Acknowledgements The study is part of NetResilience consortium and was funded by the  Strategic Research Council at the Academy of Finland  (grant number  345185  and  345183 ) (VL, AN), the  Kone Foundation  (VL) the  CNRS (CF)  and the  ANR-Labex IAST (AN). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank  L. Iso-Iivari ,  K. Pokkinen ,  A. Siitonen  and  T. Verho  for collection of the demographic data,  M. Lahdenperä ,  J. Pettay  and  I. Rickard  for management of the data files and  J. Bovet ,  B. Hatchwell  and two anonymous reviewers for helpful comments. The authors have no conflict of interests. This is contribution ISEM no 2023  083  of the  Institute of Evolutionary Sciences of Montpellier.
Objectives The objectives of this thesis are to explore in detail the cortical activity of REM by comparing it with that of the two other states of vigilance in order to feed a computational model allowing to predict the mechanisms responsible for this activity. As we saw in the Introduction, the classical view of REM largely relies on old studies and is challenged by recent findings, questioning previous conceptions about the cortical activity in REM. We will first try to describe the diversity of mouse cortical activity regimes that occur in REM by analysing a LFP dataset (Fernandez et al citeFernandez2016), looking for REM SWA and phasic theta events as described in  [36, 75]  respectively. We will present the important aspects of those very different phenomenons using standard spectral analysis, but also with a closer examination at the individual components of those phenomenons with a cycle by cycle analysis adapted from  [23]  This approach allows us to precisely describe the signals but also to tackle our second objective: questioning the spatio-temporal organisation and the co-existence of the different REM cortical regimes.
Electrophysiological signal analysis The data analyzed in the present study come from a database acquired in unanesthetized head restrained mice recorded by L F and S C. Part of this dataset has been recently analyzed and published to characterize the cortical slow waves of wakefulness and NREM  [11]. Animal preparation and surgery Briefly, nineteen C57BL/6 male mice (Janvier SAS, St. Berthevin, France), aged of 6 to 8 weeks at the time of surgery, were used for the study. All procedures were approved by the University of Lyon 1 Animal Care Committee (project DR2013-4) and were conducted in accordance with the French and European Community guidelines for the use of research animals. The data of only seven out of the nineteen mice is analyzed here. LFP for those mice were recorded at the following sites: the barrel field of the primary Somatosensory cortex (S1), the secondary Somatosensory cortex (S2), the primary Motor area (M1), the prelimbic area of the medial prefrontal cortex (mPFC), and the area CA1 of the dorsal hippocampus (dCA1).