Combination of inclusive and differential t(t)over-bar charge asymmetry measurements using ATLAS and CMS data at root S=7 and 8 TeV
Bibliographic record
Abstract
Agradecimentos: We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS and CMS could not be operated efficiently. The work presented in this paper is performed in the context of the LHC top physics working group and benefits from its important contributions. We acknowledge the support of ANPCyT (Argentina); YerPhI (Armenia); ARC (Australia); BMWFW and FWF (Austria); ANAS (Azerbaijan); SSTC (Belarus); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); NSERC, NRC, and CFI (Canada); CERN; CONICYT (Chile); CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); MSMT CR, MPO CR, and VSC CR (Czech Republic); DNRF and DNSRC (Denmark); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, HGF, and MPG (Germany); GSRT (Greece); RGC (Hong Kong SAR, China); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); ISF, ICORE, and Benoziyo Center (Israel); INFN (Italy); MEXT and JSPS (Japan); MSIP, and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); CNRST (Morocco); NWO (Netherlands); MBIE (New Zealand); RCN (Norway); PAEC (Pakistan); MNiSW, MSHE, NCN, and NSC (Poland); FCT (Portugal); MNE/IFA (Romania); JINR (Dubna); MES, MON, NRC KI, RosAtom, RAS, RFBR and RAEP (Russian Federation); MESTD (Serbia); MSSR (Slovakia); ARRS and MIZ_S (Slovenia); DST/NRF (South Africa); MINECO, SEIDI, CPAN, PCTI and FEDER (Spain); SRC and Wallenberg Foundation (Sweden); ETH Board, ETH Zurich, PSI, SERI, SNSF, UniZH, and Cantons of Bern, Geneva and Zurich (Switzerland); MOST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (United States of America). In addition, individual groups and members have received support from BELSPO, FRIA, and IWT (Belgium); BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust (Canada); the Leventis Foundation (Cyprus); MEYS (Czech Republic); EPLANET, ERC, ERDF, FP7, Horizon 2020, and Marie Sk lodowska-Curie Actions (European Union); Investissements d'Avenir Labex and Idex, ANR, R_egion Auvergne and Fondation Partager le Savoir (France); AvH Foundation (Germany); the Herakleitos, Thales, and Aristeia programmes co-financed by EU-ESF and the Greek NSRF (Greece); CSIR (India); BSF, GIF, and Minerva (Israel); BRF (Norway); the HOMING PLUS programme of the FPS, co-financed from the EU Regional Development Fund, the Mobility Plus programme of the MSHE, and the OPUS programme of the NSC (Poland); the NPRP by Qatar NRF (Qatar); Generalitat de Catalunya, Generalitat Valenciana, and the Programa Clarín-COFUND del Principado de Asturias (Spain); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Royal Society and Leverhulme Trust (United Kingdom); the A. P. Sloan Foundation, the Welch Foundation, and the Weston Havens Foundation (United States of America). The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), RRC-KI and JINR (Russian Federation), PIC (Spain), ASGC (Taipei), RAL (U.K.), and BNL and FNAL (U.S.A.), and from the Tier-2 facilities worldwide and large non-WLCG resource providers
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".