{"id":"W4398287077","doi":"10.7910/dvn/28075/ss1wjc","title":"events.2015.20170206133646.tab","year":2017,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Event data; Event (particle physics); 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":[0.0005402978,0.002340377,0.001376986,0.004164163,0.0006983808,0.003254291,0.002435781,0.001837224,0.2297663],"category_scores_gemma":[0.003317833,0.0006604519,0.001175617,0.006078833,0.0004371293,0.001782101,0.002125579,0.001409924,0.2459195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001697555,"about_ca_system_score_gemma":0.001797271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01841149,"about_ca_topic_score_gemma":0.03898516,"domain_scores_codex":[0.99945,0.00006121149,0.00007434742,0.0001635756,0.0001106408,0.0001401758],"domain_scores_gemma":[0.9988592,0.0002359802,0.0001781039,0.0002184021,0.0002804713,0.0002279361],"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":[0.00004272662,0.00001026793,0.0003723021,0.0002552137,0.000009732427,0.00001080204,0.000007744175,0.00009516443,0.00003393671,0.0003168816,0.9974946,0.001350594],"study_design_scores_gemma":[0.0002332707,0.00001885539,0.002406466,0.0002427179,0.00001931866,0.00007049255,0.00005532285,0.0003421892,0.0002438737,0.001202656,0.9951422,0.00002268432],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008702969,0.00008104605,0.00003280642,0.00006838613,0.0000383809,0.000006220464,0.9981584,0.0003316518,0.001196048],"genre_scores_gemma":[0.0004133521,0.0000791223,0.00009350498,0.00005804273,0.00002081046,0.00002349535,0.997995,0.00007668972,0.001239994],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7702338,"threshold_uncertainty_score":0.7686448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01675018954481258,"score_gpt":0.3243585113894363,"score_spread":0.3076083218446238,"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."}}