{"id":"W4398677153","doi":"10.7910/dvn/28075/5uiimb","title":"events.2019.20200427085336.tab","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science","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":[0.0007829007,0.001560812,0.001044603,0.004316667,0.0006649718,0.003095512,0.001911187,0.001619752,0.2692922],"category_scores_gemma":[0.004896736,0.0008230355,0.001038819,0.008553497,0.0003599227,0.001629198,0.001778662,0.001683894,0.2014638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001934668,"about_ca_system_score_gemma":0.002029716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03327957,"about_ca_topic_score_gemma":0.05211118,"domain_scores_codex":[0.999413,0.00007225655,0.00008326994,0.0001589639,0.0001411214,0.0001313232],"domain_scores_gemma":[0.9982231,0.0004653914,0.0003143537,0.000307861,0.0004076447,0.0002815618],"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":[0.00003661181,0.000008576472,0.0004387855,0.0003453809,0.00001158146,0.000008563315,0.000009988945,0.000146275,0.00003170886,0.0005993573,0.9969994,0.001363814],"study_design_scores_gemma":[0.0002344724,0.00001321209,0.002902451,0.000299291,0.00001674911,0.00002985784,0.00004256057,0.0002588471,0.0001563177,0.001185486,0.9948394,0.00002141734],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003037557,0.00002073276,0.00001816243,0.00004301314,0.00001543571,0.000003584936,0.9989161,0.0001174256,0.0008350423],"genre_scores_gemma":[0.0003142566,0.00005390035,0.00009172047,0.00006463318,0.0000102184,0.00003165102,0.998152,0.00006676555,0.001214846],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7307078,"threshold_uncertainty_score":0.9008723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04883943678167185,"score_gpt":0.3467607672005543,"score_spread":0.2979213304188825,"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."}}