dataesr/scientific-paragraphs-categorization huggingface dataset:
16 if there is no new physics in the data (recall that r ≡ 1/µ 95 ). The ATLAS analysis has slightly less sensitivity and is not expected to individually exclude the point (µ 95 > 1 or equivalently r exp < 1). Combining the likelihoods from both analyses (dashed blue curve in the plot), we arrive at an expected exclusion of r exp (combined) = 1. 52, which illustrates the gain in sensitivity. With the observed data, however, shown in the plot on the right, the strongest constraint comes from the ATLAS-SUSY-2018-41 analysis, excluding the point with an r obs = 1. 32. The CMS-SUS-21-002 analysis only gives an r obs value of 0. 84. The reason is that the former analysis observed a deficit of events, while the latter observed a small excess. In an analysisby-analysis approach, one could exclude the point based on the highest observed r-value, or conclude that it is still allowed because the most sensitive analysis (the one with highest r exp ) does not allow to exclude it.
Database update The database update with respect to v2. 1. 0  [7]  concerns the results from 9 ATLAS and 12 CMS publications. Concretely, the following results were added corresponding to the availability of appropriate public numerical material on HEPDATA: 5  Gluino/squark searches: In this category, we added UL results from two CMS analyses, the search in final states with jets plus leptons CMS-SUS-19-008  [33]  and the search in final states with jets plus highly boosted Z-bosons CMS-SUS-19-013  [34]. From ATLAS, we added the gluino/squark UL and EM results from the search in final states with jets plus two leptons, ATLAS-SUSY-2018-05  [35]. Stop/sbottom searches: Here we added UL and EM results from two ATLAS publications: the stop search in the 2ℓ+jets channel, ATLAS-SUSY-2018-08  [36] , and the sbottom search in final states with b-jets and taus (from h → ττ), ATLAS-SUSY-2018-40  [37].
On the CMS side, we added UL results from five publications, namely the 0ℓ and 2ℓ stop searches CMS-SUS-19-010  [38]  and CMS-SUS-19-011  [39] , the stop results from the search with soft leptons, CMS-SUS-18-004  [40] , the sbottom results from CMS-SUS-18-007  [41] , which uses h → γγ decays, as well as the stop combination CMS-SUS-20-002  [42]. All these are results for full Run 2 luminosity: 139/fb for ATLAS and 137/fb for CMS. Moreover, lacking other EM results from CMS, we added the EMs which became available for CMS-SUS-16-050  [43] , i. e. the 0ℓ stop search for 35. 9/fb. Electroweak searches: These constitute the most important part of this database update. We added UL and EM results from ATLAS covering a large variety of EW-ino searches at Run 2; these are four searches in leptonic channels,  6  ATLAS-SUSY-2018-05 (2 leptons)  [35] , ATLAS-SUSY-2018-32 (2 OS leptons)  [29] , ATLAS-SUSY-2019-02 (soft leptons)  [44]  and ATLAS-SUSY-2019-09 (3 leptons)  [30] , as well as the fully hadronic search ATLAS-SUSY-2018-41  [31].
On the CMS side, the most important additions are the UL and EM results from the fully hadronic EW-ino search, CMS-SUS-21-002  [32] , and the search for higgsinos decaying to Higgs bosons (with h → b b), CMS-SUS-20-004  [23]. Both analyses provide correlation and covariance matrices allowing for SRs combination. While the purely Gaussian SLv1  [9]  works well for CMS-SUS-21-002, the CMS-SUS-20-004 analysis has highly asymmetric background uncertainties in several SRs, which makes the SLv1 a bad approximation of the full likelihood  [22]. Thankfully, the background uncertainties are reported in non-symmetrised form in the paper  [23] , and we can use this information to compute the skew terms for the SLv2; CMS-SUS-20-004 is thus the first analysis in the SMODELS database which uses the SLv2 following  [18].
Long-lived particles: The previous database already contained a large number of results from searches for long-lived particles, as these had been the primary target of the v2. 1. 0 update  [7]. In v2. 3. 0, one new analysis is added to this: the 139/fb ATLAS search for heavy, longlived, charged particles with dE/dx measurement, ATLAS-SUSY-2018-42  [48]. Concretely, we implemented the UL results for HSCPs (long-lived staus) and R-hadrons (long-lived gluinos) together with R-hadron EMs for the two inclusive SRs; the implementation of an EM binning in target mass is left for a future release. The analysis has also considered simplified models for long-lived charginos, but, unfortunately, the material provided for this is not sufficient for being used by SMODELS  [49]. Recast with MadAnalysis5: In addition to the UL and EM results listed above, which were directly provided by ATLAS and CMS, we generated a number of "home-grown" EMs through recasting with MADANALYSIS 5  [50, 51].
This helps fill gaps in EM coverage whenever a MAD-ANALYSIS 5 recast code is available. Concretely, we produced EMs for the 35. 9/fb leptonic EW-ino searches CMS-SUS-16-039  [52, 53]  and CMS-SUS-16-048  [54, 55] , and the 137/fb multi-jet gluino and squark search CMS-SUS-19-006  [56, 57]. All three analyses come with a covariance matrix for SR combination in the SLv1 approach. The quality of the recasts is documented in  [13]. With these additions, the SMODELS v2. 3. 0 database now comprises results from 38 ATLAS and 40 CMS searches at Run 2 ( s = 13 TeV), as well as 15 ATLAS and 18 CMS searches at Run 1 ( s = 8 TeV), covering a total of 111 experimental publications. 17 of the AT-LAS and 13 of the CMS searches are for full Run 2 luminosity. An overview of the complete database is given in Tables 1-3 in Appendix A and online at https://smodels.
Physics impact Since the largest part of the database update concerns searches for charginos and neutralinos, we demonstrate the physics impact of the new database and the new features in SMODELS by means of constraints on the EW-ino sector of the MSSM. To this end, we reuse the EW-ino scan points from  [7]. In section 4. 2 of that paper, the relevant Lagrangian parameters, i. e. the bino and wino mass parameters M 1 and M 2 , the higgsino mass parameter µ, and tan β = v 2 /v 1 were randomly scanned over within the following ranges: 10 GeV < M 1 < 3 TeV , 100 GeV < M 2 < 3 TeV , 100 GeV < µ < 3 TeV , (7) 5 < tan β < 50. The other SUSY breaking parameters were fixed to 10 TeV. 8  The lower limits on M 2 and µ were chosen so as to avoid the LEP constraints on light charginos, and the lightest neutralino χ0 1 was required to be the lightest supersymmetric particle (LSP).
Finally, there is also a significant increase in the number of excluded points at low m χ± 1 ≲ 200 GeV; these are to large extent points with a higgsino-like LSP. Overall, the number of excluded points increases from 661 with v2. 1. 0 to 2974 (1787) with v2. 3. 0 when SR combination is turned on (off). Given Figure  2 , it is interesting to ask which experimental results are driving the exclusion in different regions of the parameter space. To answer this question, Figure  3  shows the points excluded by the LHC searches in the SMODELS v2. 3. 0 database in the m χ± 1 versus m χ0 1 plane. The color of each excluded point denotes the most constraining analysis, that is the analysis giving the highest observed r-value. As can be seen, in the low mass range, m χ± 1 ≲ 500 GeV, the constraints come from a variety of different analyses, while the high mass range, m χ± 1 ≳ 500 GeV is completely dominated by the ATLAS-SUSY-2018-41 analysis.
Conclusions The wealth of experimental constraints on new physics from Run 2 of the LHC makes it increasingly important to be able to perform global (re)interpretation studies. The aim is to find out on the one hand how all the available experimental information constrains complicated scenarios, and on the other hand what are the most likely regions where new physics can still manifest itself at the LHC. This requires software tools that encompass searches in as many final states as possible, and that can build global likelihoods from them. The SMODELS package is a public tool that can be useful in this endeavour. It is designed for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results, mostly stemming from searches for supersymmetric particles.
Owing to its speed, it is particularly well suited for large scans and model surveys. Version 2. 3 of SMODELS, presented in this paper, comes with a significant database update with the latest available experimental results for full Run 2 luminosity. In total, the SMODELS database now comprises results from 38 ATLAS and 40 CMS searches at Run 2, as well as 15 ATLAS and 18 CMS searches at Run 1, covering a total of 111 experimental publications. 17 of the ATLAS and 13 of the CMS searches are for full Run 2 luminosity. This comprises in particular the full suit of available and reusable electroweak-ino searches from Run 2. On the code side, the most important new feature is the ability to combine likelihoods from different analyses. This enables statistically more rigorous constraints and opens the way for global likelihood analyses for LHC searches.
We reviewed the different ways of computing likelihoods for a signal hypothesis from the publicly available information, and explained how likelihoods can be combined in SMODELS. The physics impact was demonstrated by means of a case study of the electroweak-ino sector of the MSSM. With the new database, the reach in chargino mass increases from about 650 GeV in  [7]  to about 1 TeV in v2. 3. 0. for promptly decaying EW-inos. The total number of excluded points increases by more than a factor of four with respect to  [7]. Last but not least, we exemplified how the combination of approximately independent analyses helps average out statistical fluctuations and thus leads to more robust limits. An extensive global study of the EW-ino sector of the MSSM is in preparation. Data management: SMODELS is public software distributed under the GNU General Public License v3 (GPLv3).
A Complete list of experimental results in the v2. 3. 0 database Table  1 : List of ATLAS Run 2 analyses and their types of results in the SMODELS v2. 3. 0 database. Apart from the HSCP, disappearing tracks and displaced lepton searches, all analyses require / E T in the final state (for conciseness omitted in the short descriptions). EWK stands for electroweak (-ino or slepton) production. The last column specifies whether and how SRs are combined. New additions are highlighted in bold. 2 SS ℓ 35. 9 ✓ CMS-SUS-16-036  [102]  0ℓ + jets 35. 9 ✓ ✓ CMS-SUS-16-037  [103]  1ℓ + jets with MJ 35. 9 ✓ CMS-SUS-16-039  [52]  multi-ℓ, EWK 35. 9 ✓ ✓ SLv1 CMS-SUS-16-041  [104]  multi-ℓ + jets 35. 9 ✓ CMS-SUS-16-042  [105]  1ℓ + jets 35. 9 ✓ CMS-SUS-16-043  [106]  W h(b b), EWK 35. 9 ✓ CMS-SUS-16-045  [107]  2 b + 2 h(γγ) 35.
B Database add-ons When running SMODELS, the user has to specify which database to use. This is done in the parameters. ini file by giving the path to the smodels-database folder, the path to a pickle file or a URL path. Details are explained in the Using SMODELS section of the online manual. The available databases can be seen on the smodels-database-release page on github. Shorthand notations are available: path=official refers to the official database of the users' SMODELS version, while path=latest refers to the latest available database release. The '+' operator allows for extending the "official" or "latest" database with add-ons: +fastlim: adds fastlim results (from early 8 TeV ATLAS analyses); from v2. 1. 0 onward +superseded: adds results which were previously available but were superseded by newer ones; from v2.
1. 0 onward +nonaggregated: adds analyses with non-aggregated SRs in addition to the aggregated results in CMS analyses; from v2. 2. 0 onward +full_llhds: replaces simplified HISTFACTORY statistical models by full ones in ATLAS analyses; from v2. 3. 0 onward (careful, this increases a lot the runtime!) Examples are "official+nonaggregated" or "official+nonaggregated+full_llhds". Note that order matters: results are replaced in the specified sequence, so "full_llhds+official" will fall back onto the official database with simplified HISTFACTORY statistical models. The add-ons can also be used alone, e. g. path=full_llhds, though this is of limited practical use. Finally, "debug" refers to a version of the database with extra information that is however not intended for usage by a regular user and only mentioned here for completeness.
C Examples combining more than two analyses The benchmark point used in Fig. 1  is in fact constrained not only by the two hadronic EWino searches ATLAS-SUSY-2018-41  [31]  and CMS-SUS-21-002  [32] , but also by the search for χ± 1 χ0 2 → W h + / E T in the 1ℓ + b b + / E T final state, ATLAS-SUSY-2019-08  [28]  (other analyses in the SMODELS database do not give any relevant constraints). Assuming that hadronic searches (vetoing leptons) and leptonic ones (requiring leptons) are independent to good approximation, we can combine all three analyses. The result is shown in Fig. 9. The combined sensitivity increases to r exp = 1. 67, while the observed r-value moves to r obs = 1. 32, compared to r exp = 1. 52 and r obs = 1. 41 in Fig. 1. Figure  10  shows an EW-ino scenario with a bino-like χ0 1 with mass around 140 GeV, and higgsino-like χ0 2,3 , χ± 1 with masses around 555 GeV; the wino-like states have masses of 2.
Acknowledgments We thank  Jack Araz  for identifying an issue with the expected upper limits in the simplified likelihoods computation and for help with cleaning up the statistics code. Moreover, we thank  Shion Chen  for very useful exchanges on ATLAS-SUSY-2018-41, and  William Ford  for in-depth discussions on CMS-SUS-20-004 and the  SLv2 , and for assuring that additional auxiliary material was made available on HEPDATA for this analysis. We also gratefully acknowledge numerous exchanges with the ATLAS and CMS SUSY group conveners: their engagement is remarkable, and we hope they will not tire responding to our questions and/or forwarding them to the relevant people. Last but not least, we warmly thank all  ATLAS  and CMS analysers who ensure that their work can be reused by others-without their dedication to make extensive auxiliary material available on HEPDATA, public tools like SMODELS, and reinterpretation studies in general, would not be possible.
Funding information This work was supported in part by the  IN2P3  master project " Théorie -BSMGA ", by the  French Agence Nationale de la Recherche (ANR)  under grant  ANR-21-CE31-0023  ( PRCI   SLDNP ) and the  Austrian Science Fund (FWF)  under grant number  I 5767-N. T. P. is supported by the  Initiatives de Recherche à Grenoble Alpes  ( IRGA )  ANR-15-IDEX-02  project no. G7H-IRG21B26  (APM@LHC). A. L. is supported by  FAPESP  grant no. 2018/25225-9  and  2021/01089-1.
Figure10shows an EW-ino scenario with a bino-like χ01 with mass around 140 GeV, and higgsino-like χ0 2,3 , χ± 1 with masses around 555 GeV; the wino-like states have masses of 2. 3 TeV. This scenario is not excluded by any of the individual analyses in the SMODELS v2. 3. 0 database. Concretely, we get the following results: Analysis final state r exp r obs CMS-SUS-21-002 hadronic 0. 81 0. 63 ATLAS-SUSY-2018-41 hadronic 0. 63 0. 92 ATLAS-SUSY-2018-05 2ℓ + jets 0. 46 0. 68 ATLAS-SUSY-2019-08 1ℓ + b b 0. 42 0. 27Again, the highest sensitivity comes from the hadronic EW-ino searches ATLAS-SUSY-2018-41 and CMS-SUS-21-002, and combining them one reaches r exp = 1. 06 and r obs = 1. 05. In Fig. 6.
46, 47 There is not much research in agricultural and animal husbandry societies using this technology that compares both sexes across all kinds of physical activity in ecological contexts where high physical activity levels are indispensable to survive and reproduce. Despite the centrality of physical work in everyday life and the strikingly gendered nature of work in most populations, theories of how family structure influences the sexual division of labor are not well developed. 2] [53] [54]  It is possible that dispersal away from kin reduces cooperation and hence work for the household, or alternatively, the lack of kin support may reduce bargaining power and thus increase workload. Here we collected accelerometer and time-budget data on workload from 561 people from a range of rural households in southwestern China.
We explore the ecological diversity and different social structures found in southwestern China to examine whether sexbiased dispersal is a disadvantage to the dispersing sex. Technological advances arising from wearable fitness tracking enable us to address these questions on a much larger scale than was previously possible. The accelerometer data provide accurate measures of work rate. The time-budget data, while a more subjective measure based on recall, give insights into how the emerging differences in workload arise. This is the first large-scale comparison across different ecologically relevant contexts that examines how workload (overall physical activity) is determined by sex-specific demography and dispersal.
Our response variable, step counts per day (the proxy of overall daily workload) formed an age-dependent inverted U-pattern between sexes; as individuals grew older, their workload increased to reach a peak around their later mid-life, followed by a gradual decline until end of their lives, presented by raw person-level and day-level data (Figure  S2 ). We generated multiple days of step-count data from the same person based on their full days wearing the accelerometer (days worn) and performed multilevel analysis for the day-level activity data (n = 640 days). As seen in Figure  2 , individuals aged over 60 made a labor contribution of 4,578 fewer daily step counts than those aged 30-40 and 3,386 fewer than those aged 40-50. There was no substantial difference between the oldest ZhAmdo, mqAmdo, Zhaba, Mosuo, Han, and Yi represent six ethnic groups.
Differences in activity budgets by sex In addition to the data from the accelerometer, we collected time budgets by recall over the last 24 h. While this is a more subjective form of data, it enables us to see how it is that some of the differences in activity that we have described may emerge. Females are more active and spend more time than males in all work intensities of agricultural and animal husbandry, according to self-reported time budgets (Figure  5 ). Females spent significantly more time than males doing moderate-intensity domestic chores like cooking, laundry, and sweeping (mean, male = 18. 247; mean, female = 88. 402), as well as some moderate-intensity agricultural and animal husbandry work like cow feeding and milking (mean, male = 59. 584; mean, female = 91. 243). Women also appear to spend more time on ''gathering'' activities (Figure  5 ) (mean, male = 11.
Strengths and limitations The research design minimizes response and sampling bias in a cross-cultural, quasi-natural setting, collected across many villages and locations with diverse social structures within one country. Data are obtained independently from reported time budgets and activity trackers, presenting consistent results. The comparative nature of the data warrants the conclusion that being female and being the dispersing sex both mean one works more. Compared with observational scans, the step counts, equivalent to 1,440 min per day, validated by time budget, enable us to fully capture the full activity expenditure of individuals, not only during the day but also at night. One important limitation of this study is that the association between workload and fitness is hard to resolve.
82 We compared Miband2 and Fitbit as alternatives, selected for both practicality and technical support. 82 Some studies suggest that Miband2 is not intensely stable, compared with Fitbit, as Miband2 mainly relies on arm swing. 47 However, activities such as milking which includes moments without walking to produce energy expenditure are also included in our research. Compared to Fitbit, we observed that Miband2 is more stable with the lowest variation coefficient when multiple trackers were subjected to the same activity in a test (see Table  S1 ). Step counts are raw activity data derived from arm movement, whereas some studies use activity counts (AC) calculated depending on combining the basal metabolic rate and step counts. Previous studies revealed that activity counts might be inappropriate for studying physical activity since they underestimated the daily physical activity energy expenditure (PAEE) when activity counts were converted into kilocalories per kilogram per day.
Acr ss all these samples, we got data from 536 married people in total (see Table  1 ). We obtained 561 people's activity data and time-budget data, after omitting invalid step count data, 536 activity data in the last 24 hrs were left. Among all 561 individuals, 469 were married people with demographic data, and 438 of them provided activity data in the last 24 hrs (Men = 194, women = 244). Six hundred and forty activity observations were provided by 438 married individuals. And we obtained 569 daily observations of 469 married individuals, down to 566 of 394 married people when constructed models with missing data omitted. Some of those interviewed for demographic information were randomly asked to participate in the activity research. They were asked to wear a plastic removable accelerometer on their left hand (which we refer to as a wristband).
Other adult family members in the same household at that time who also agreed to wear wristbands were also included in the study. Participants were asked to wear the wristband all day and night until we collected them 2-5 days later. That time span was chosen because a shorter time estimates the daily activity pattern of participants less accurately (for instance, on rainy days, people sometimes work much less); and after 5 days, the recovery rate declines, and the wristbands lose power or can be lost. 32, 46, 86 n collection, we conducted a questionnaire on the last 24-hour time budget by recall. This enabled us to validate the electronically collected data on activity by comparing it with the participants' account of their activity over the last day (see also Table  S2 ). We asked if they had removed the wristband at any time.
Validation of activity trackers in the field For data validation of wristbands in the field, we randomly recruited three persons in one family to track their daily lives for 24 hours. When we began to follow them, the old male had not woken up, however, the two females (mother-in-law and daughter-in-law) had already started their work. (also see Figure  S1. ) The younger female's sleep time we recorded was at 10:10 pm and got up at 5:31 am, which was consistent with what the wristband sleep data showed. In general, females woke up earlier than males. Notably, this younger woman felt a bit unwell and rested at some point in the day. Hence, the workload on that day was lower than usual.
Model validation of step counts Most previous studies of workload obtained activity information through self-report. 26, 27, 59, 87 Although data obtained from questionnaires is effective, participants frequently ignore some light activity when self-reporting, and participants' physical, cognitive, and emotional conditions can all influence reporting. We used 24-hour recall to estimate time budgets to record time spent on each activity (see Table  S7 , supplemental information). Combined with the time-budget questionnaire, we used wearable activity trackers to provide more detailed information, including activity frequency, activity duration time, and 'steps' or amount of activity. Studies of metabolic energy consumption have found that the costs of daily activities vary greatly. We recorded 24 categories of activity in total (see supplemental information: Activity code -Table  S7 ) depending on different metabolic equivalent tasks levels (METs), which refers to the ratio of the energy expenditure rate for activity compared to resting energy expenditure, and we classified some subsistence-related activities into L.