{"id":"W4398652921","doi":"10.7910/dvn/28075/zhriy8","title":"events.2020.20200506093336.tab.zip","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Legal and Regulatory Analysis","field":"Social Sciences","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006308137,0.0004090029,0.0006336592,0.0001304877,0.0008109604,0.0001888721,0.001645876,0.0005523287,0.05755785],"category_scores_gemma":[0.0004988655,0.0003808419,0.0004353447,0.0007752934,0.000382849,0.0005135679,0.0005587468,0.0006859067,0.3562943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991382,"about_ca_system_score_gemma":0.0006705151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00582274,"about_ca_topic_score_gemma":0.00473385,"domain_scores_codex":[0.9964746,0.0004971374,0.0004538483,0.0008126199,0.001134252,0.0006275868],"domain_scores_gemma":[0.9978076,0.00009828688,0.0003158386,0.00107452,0.0001006117,0.0006031694],"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":[0.00002542252,0.00007472961,0.00001552298,0.00006559336,0.0002322897,0.0002138831,0.0002904838,0.000003806994,0.000003078168,0.000214443,0.9986464,0.0002143663],"study_design_scores_gemma":[0.0002414011,0.00002379478,0.00002406979,0.00004943732,0.0004768443,0.000002473846,0.0005118927,0.000005080126,0.000003013347,0.0001091712,0.9980682,0.0004846113],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000008604932,0.000005355977,0.000008111746,0.0003239786,0.00144645,0.0002820831,0.9959773,0.0001205377,0.001827544],"genre_scores_gemma":[0.0000468993,0.001264899,0.00007307024,0.001406723,0.003489456,0.00002037085,0.988003,0.00002424218,0.0056713],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2987365,"threshold_uncertainty_score":0.9998643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737240111043369,"score_gpt":0.2715572295697545,"score_spread":0.2541848284593208,"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."}}