{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000510612,0.0005264755,0.0006200242,0.0006428135,0.0002306799,0.0001406291,0.007214472,0.0007496962,0.01515439],"category_scores_gemma":[0.0003088918,0.0004149772,0.0002579394,0.0005481036,0.0001713603,0.001435117,0.003991333,0.0005960855,0.3785303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001090574,"about_ca_system_score_gemma":0.0002246125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001879511,"about_ca_topic_score_gemma":0.0001296832,"domain_scores_codex":[0.9967356,0.0001494788,0.0005135893,0.001309915,0.0005937556,0.0006976668],"domain_scores_gemma":[0.9918446,0.0001363583,0.000401054,0.007294127,0.0000992939,0.0002245818],"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":[0.000008095413,0.0000786642,0.000008161064,0.00002712725,0.0001729792,0.0001628582,0.000003614063,2.034942e-7,0.00001008246,0.001206892,0.9959977,0.002323589],"study_design_scores_gemma":[0.0004155759,0.00004313461,0.0000217308,0.00008368203,0.0001514584,0.00004452668,0.000003714447,0.00003132108,0.00004147337,0.001034866,0.9975744,0.0005540812],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[8.660778e-7,0.000005720029,0.03645368,0.0001416054,0.001192499,0.0001694863,0.961534,0.0003636553,0.0001384396],"genre_scores_gemma":[0.000005293648,0.0006794935,0.002882145,0.0006383898,0.000336053,0.00003812494,0.9940221,0.00001818925,0.001380251],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3633759,"threshold_uncertainty_score":0.9998302,"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."}}