{"id":"W4200634628","doi":"10.1103/physrevlett.128.241802","title":"MicroBooNE and the <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:msub><mml:mrow><mml:mi>ν</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> Interpretation of the MiniBooNE Low-Energy Excess","year":2022,"lang":"lv","type":"article","venue":"Physical Review Letters","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"H2020 Marie Skłodowska-Curie Actions; Science and Technology Facilities Council; Ontario Ministry of Research, Innovation and Science; Government of Canada; Innovation, Science and Economic Development Canada; Harvard University; Ohio State University; Horizon 2020 Framework Programme; Alfred P. Sloan Foundation; U.S. Department of Energy","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001068877,0.0007535869,0.000515982,0.0009406906,0.0008465141,0.002248568,0.001667184,0.001415644,0.2325953],"category_scores_gemma":[0.003418091,0.0004984468,0.0006672556,0.001612555,0.0005112733,0.00361005,0.001170031,0.001648243,0.0777746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001155374,"about_ca_system_score_gemma":0.0009277769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005862517,"about_ca_topic_score_gemma":0.01212006,"domain_scores_codex":[0.9994555,0.0001104753,0.0000176794,0.00009440317,0.0002455006,0.00007646256],"domain_scores_gemma":[0.9988367,0.0004759351,0.00009199063,0.0002553846,0.0001855549,0.0001543907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001713355,0.000030867,0.0003192745,0.0002393526,0.00001274898,0.000181387,0.00009399643,0.0002076371,0.002036832,0.06230812,0.8821926,0.05220588],"study_design_scores_gemma":[0.00005311447,0.00001683058,0.0005383349,0.00003208748,0.000004649889,0.0001162449,0.00002734698,0.001388462,0.002700782,0.01044382,0.9846602,0.00001811425],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01135563,0.0057545,0.08707804,0.04520039,0.006323852,0.0002457966,0.03881265,0.04335883,0.7618703],"genre_scores_gemma":[0.1090742,0.007925867,0.1647794,0.009314494,0.001805099,0.0009865167,0.06871434,0.0266192,0.6107809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2325953,"threshold_uncertainty_score":0.7781088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128472065739401,"score_gpt":0.2297385369533819,"score_spread":0.2184538162959879,"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."}}