{"id":"W6902697062","doi":"10.7910/dvn/28075/rtpciv","title":"Events.2017.20200602.tab.zip","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Event (particle physics); Product (mathematics); Identification (biology); Process (computing)","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":[0.0007600964,0.00182933,0.001140631,0.005417539,0.0007253336,0.003262814,0.001801587,0.001527679,0.2424555],"category_scores_gemma":[0.005347984,0.0008659711,0.0009201979,0.01064032,0.000394945,0.002026362,0.001950699,0.001568496,0.2579908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001787984,"about_ca_system_score_gemma":0.001891369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02901656,"about_ca_topic_score_gemma":0.04204291,"domain_scores_codex":[0.9992798,0.00008150004,0.0000895218,0.0001959746,0.0001754766,0.0001776753],"domain_scores_gemma":[0.9978533,0.0005482791,0.0003181986,0.0004261325,0.0005623091,0.000291672],"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":[0.00002345497,0.000006309666,0.000308565,0.0001832187,0.00000666897,0.000007153397,0.00001029976,0.00008190932,0.00002847046,0.0005481944,0.9978376,0.0009580262],"study_design_scores_gemma":[0.0001101086,0.000007878277,0.002172713,0.0001683682,0.00001028969,0.00002444137,0.00004820142,0.0001481979,0.0001615643,0.0009437097,0.9961885,0.00001610719],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003589523,0.00002162286,0.00002300765,0.00003778225,0.00002039524,0.000003195659,0.9987534,0.000145695,0.0009590198],"genre_scores_gemma":[0.0002852614,0.00004825194,0.00007868838,0.00004278205,0.00001261448,0.00002284575,0.9981013,0.0001049313,0.001303392],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7575445,"threshold_uncertainty_score":0.8110946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0312886550703533,"score_gpt":0.2683487666026759,"score_spread":0.2370601115323226,"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."}}