{"id":"W4398299035","doi":"10.7910/dvn/28075/3z3phu","title":"events.2014.20151111085638.tab","year":2015,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Event (particle physics); Event data; Computer science; Data science; Physics; Analytics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008628841,0.000534589,0.0006821901,0.0006502805,0.000187249,0.0001848339,0.007388238,0.0007685426,0.005388629],"category_scores_gemma":[0.000376663,0.0005062079,0.0002163445,0.0006767723,0.000149242,0.001364351,0.004239005,0.0008671039,0.4261005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001434905,"about_ca_system_score_gemma":0.0004062443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005295936,"about_ca_topic_score_gemma":0.0002599864,"domain_scores_codex":[0.9965935,0.0001766558,0.0005176175,0.001279193,0.0007884791,0.0006445122],"domain_scores_gemma":[0.9917809,0.00008063253,0.0003962416,0.00723626,0.0001834005,0.0003225747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009085298,0.0001059158,0.00001105577,0.00002958328,0.0001551594,0.0001992399,0.000008618193,0.000002524262,0.00000203059,0.000594511,0.9979022,0.0009800351],"study_design_scores_gemma":[0.0004052658,0.0000592275,0.00001494125,0.00003409029,0.0001887098,0.00006172105,0.00001291612,0.000175992,0.00001172935,0.00115055,0.9973233,0.0005614991],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002007628,0.00001030737,0.0113986,0.00006097418,0.001269635,0.0001845295,0.9865329,0.000389474,0.0001515194],"genre_scores_gemma":[0.000004564506,0.0004312734,0.00545019,0.0006216608,0.0002800359,0.00003252894,0.9926482,0.000017779,0.0005137923],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4207118,"threshold_uncertainty_score":0.9997389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652169801569527,"score_gpt":0.2496285955985369,"score_spread":0.2331068975828416,"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."}}