{"id":"W4398276211","doi":"10.7910/dvn/28075/6mczry","title":"events.2015.20160311094104.tab","year":2016,"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","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006478979,0.001942944,0.001326917,0.00485744,0.0007386766,0.003225875,0.002033117,0.001579723,0.221448],"category_scores_gemma":[0.004149387,0.0008025332,0.001007576,0.008811292,0.0003852909,0.001742838,0.001867908,0.001656946,0.2175642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001876559,"about_ca_system_score_gemma":0.002099889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02243847,"about_ca_topic_score_gemma":0.03886362,"domain_scores_codex":[0.9993788,0.00005854433,0.0001021458,0.0001760224,0.0001387194,0.0001457902],"domain_scores_gemma":[0.9984381,0.0003311539,0.0002739637,0.000288926,0.0004071321,0.0002606562],"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":[0.00005033282,0.00001118594,0.0005268183,0.0004052396,0.0000119821,0.00001341458,0.00001502438,0.0001181613,0.00005475861,0.0005844617,0.996707,0.001501726],"study_design_scores_gemma":[0.0002507256,0.00001497784,0.003223801,0.0002871284,0.00001705417,0.0000484278,0.00006054832,0.0001896971,0.0002089318,0.001073575,0.9946051,0.00001990979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005372735,0.0000352534,0.00002033269,0.0000369744,0.00002066425,0.000004881349,0.9989272,0.0001426489,0.0007583497],"genre_scores_gemma":[0.000308703,0.00006370448,0.00007755426,0.00003859236,0.00001287329,0.00002933458,0.9982461,0.00006615322,0.001156997],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.778552,"threshold_uncertainty_score":0.7408174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01052766543969001,"score_gpt":0.2445342805162481,"score_spread":0.2340066150765581,"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."}}