{"id":"W4399017085","doi":"10.7910/dvn/sgfrya/adbmtc","title":"ai_importance_md.RData","year":2019,"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":"Institute on Governance","funders":"","keywords":"Replication (statistics); Psychology; Computer science; Artificial intelligence; Data science; Demography; Biology; Virology; Sociology","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":[0.0007865711,0.0002833205,0.0004704852,0.0001760824,0.00048897,0.0002235206,0.001588946,0.0004781913,0.0959586],"category_scores_gemma":[0.0002670122,0.0002533401,0.0002391566,0.0003873254,0.0003984242,0.0006206637,0.000381026,0.0004392178,0.3263671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001385743,"about_ca_system_score_gemma":0.000599351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005869595,"about_ca_topic_score_gemma":0.005044452,"domain_scores_codex":[0.9974697,0.0002315407,0.0003401333,0.0006034715,0.0008411455,0.0005140252],"domain_scores_gemma":[0.9974554,0.00007855509,0.000290321,0.001843785,0.00008494811,0.0002470564],"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":[0.000009919269,0.00004767511,0.00002842444,0.00004552041,0.0001229815,0.00006584977,0.0001020383,0.000002745501,0.000001836588,0.0002762334,0.9991798,0.000116909],"study_design_scores_gemma":[0.0001532834,0.00001153469,0.00001581611,0.00004712976,0.0002868045,0.000001363713,0.0003319993,0.000002658759,0.000002171147,0.0000437071,0.9987401,0.0003634541],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001198854,0.000004533386,0.000004793245,0.00004333359,0.00159493,0.0002369674,0.9951507,0.00004899758,0.002903787],"genre_scores_gemma":[0.00001740155,0.00101957,0.00003944865,0.0005751702,0.001016138,0.000009224224,0.9840436,0.00001470079,0.01326477],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2304085,"threshold_uncertainty_score":0.9999919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663533950056393,"score_gpt":0.2917667822259262,"score_spread":0.2651314427253623,"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."}}