{"id":"W4287182783","doi":"10.48550/arxiv.2105.01160","title":"The Tracking Machine Learning challenge : Throughput phase","year":2021,"lang":"en","type":"preprint","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fermilab; Deutsches Elektronen-Synchrotron; Université de Genève; Bundesministerium für Bildung und Forschung; Institut national de recherche en informatique et en automatique (INRIA); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; Université Paris-Saclay; European Commission; National Science Foundation; Nvidia; CERN; DeepMind","keywords":"Throughput; Tracking (education); Computer science; Phase (matter); Artificial intelligence; Machine learning; Telecommunications; Psychology; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02200606,0.001725725,0.002412051,0.0008491805,0.00229641,0.005115217,0.003851926,0.004074551,0.01050409],"category_scores_gemma":[0.09381387,0.0007510948,0.001298953,0.001308322,0.002368238,0.009229162,0.008503713,0.008980529,0.005713772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002656845,"about_ca_system_score_gemma":0.004027066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366729,"about_ca_topic_score_gemma":0.003130068,"domain_scores_codex":[0.9739551,0.01015296,0.000978816,0.003749148,0.009347997,0.001816],"domain_scores_gemma":[0.9325964,0.04542471,0.001241881,0.009860057,0.008225752,0.002651044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006103511,0.002659779,0.006564685,0.002212496,0.0004669529,0.0008606082,0.0028573,0.06660065,0.01968156,0.09966375,0.3748422,0.4174866],"study_design_scores_gemma":[0.001836369,0.00272779,0.00767889,0.0004310878,0.000121073,0.001247675,0.00255991,0.5466923,0.03430098,0.1996465,0.2023827,0.000374821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2275845,0.005014563,0.6285558,0.04343401,0.008444037,0.003237364,0.01030842,0.01734811,0.05607313],"genre_scores_gemma":[0.6708462,0.001117648,0.2790732,0.005535253,0.002240113,0.004433493,0.01172506,0.002884349,0.02214473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02200606,"threshold_uncertainty_score":0.1163806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330555548770611,"score_gpt":0.2789731446054319,"score_spread":0.2456675891177258,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}