{"id":"W7071348665","doi":"","title":"Search for Long-Lived Particles in e<sup>â§</sup>{+}e<sup>â§</sup>{-} Collisions","year":2015,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"SLAC National Accelerator Laboratory; Institut National de Physique Nucléaire et de Physique des Particules; Centre National de la Recherche Scientifique; Bundesministerium für Bildung und Forschung; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; European Commission; Alfred P. Sloan Foundation; Deutsche Forschungsgemeinschaft; U.S. Department of Energy; National Science Foundation","keywords":"Branching fraction; Hadron; Detector; Luminosity; Branching (polymer chemistry); Distribution function; Product (mathematics); Momentum (technical analysis); Particle decay","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056074,0.0004004509,0.0003120158,0.0008513121,0.0006852239,0.001085722,0.0005679392,0.0006135119,0.002692971],"category_scores_gemma":[0.0005538579,0.0003088491,0.0004348905,0.0006788643,0.0002460507,0.000464477,0.0008465806,0.0002907066,0.001050471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003142102,"about_ca_system_score_gemma":0.0003291586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005474054,"about_ca_topic_score_gemma":0.001534854,"domain_scores_codex":[0.9998067,0.00002866436,0.00001024377,0.00006859934,0.00004872342,0.00003711444],"domain_scores_gemma":[0.9996467,0.0001177396,0.0001079793,0.00003839819,0.00003182417,0.0000573549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00202174,0.0005397874,0.6666182,0.0001915598,0.0005254645,0.002832584,0.0003537005,0.006224326,0.2515668,0.01442042,0.003433843,0.05127154],"study_design_scores_gemma":[0.0005369895,0.002460032,0.3862179,0.0001145619,0.0006391216,0.008035968,0.001011864,0.1428816,0.40388,0.0182461,0.03571217,0.0002636057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840271,0.0003645676,0.01012812,0.0001043346,0.00002111099,0.00002265968,0.0006786679,0.000211074,0.004442297],"genre_scores_gemma":[0.9892777,0.00009325986,0.007094897,0.00008269784,0.00001140131,0.00001291997,0.001808936,0.000033669,0.001584552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002692971,"threshold_uncertainty_score":0.009008884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03107508728617253,"score_gpt":0.2642960752728379,"score_spread":0.2332209879866654,"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."}}