{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008615383,0.002004504,0.001316212,0.004798388,0.0007875376,0.003236795,0.002431651,0.00171213,0.2066165],"category_scores_gemma":[0.005248475,0.0009185917,0.001035202,0.008490038,0.0004388545,0.001742239,0.002151734,0.001794326,0.2029161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001903185,"about_ca_system_score_gemma":0.00238301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02286523,"about_ca_topic_score_gemma":0.04070163,"domain_scores_codex":[0.9992172,0.00008560852,0.0001277582,0.0002098676,0.0001741341,0.0001854454],"domain_scores_gemma":[0.9980167,0.0004418558,0.0003111964,0.0004167339,0.000511848,0.0003016101],"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":[0.00004268498,0.00001104272,0.0004448446,0.0003297438,0.00001219849,0.00001160694,0.00001406698,0.0001165136,0.00005247414,0.0005282106,0.9972281,0.001208526],"study_design_scores_gemma":[0.0002412369,0.00001647196,0.003455784,0.0002439488,0.0000180245,0.00004125842,0.00005514819,0.0002365015,0.0002499114,0.001208027,0.994211,0.00002280367],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005382583,0.0000277419,0.00002888365,0.00004084221,0.00002106427,0.00000557799,0.9989845,0.0001785187,0.0006589688],"genre_scores_gemma":[0.0002565917,0.00004413823,0.00008182069,0.00003746838,0.000009610086,0.00002974527,0.9986036,0.00006536839,0.0008715712],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7933835,"threshold_uncertainty_score":0.691201,"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."}}