{"id":"W4398944502","doi":"10.7910/dvn/rj7vuq/kg4sl4","title":"Contract intensity.tab","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Law, Economics, and Judicial Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Replication (statistics); Productivity; Intensity (physics); Database; Business; Economics; Computer science; Macroeconomics; Mathematics; Statistics","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":[0.001033357,0.003021373,0.001876666,0.006139573,0.0009042951,0.004791514,0.004017951,0.003562575,0.2054202],"category_scores_gemma":[0.007683254,0.001001549,0.001525807,0.0103803,0.0005680337,0.002459158,0.00272875,0.00286491,0.2041643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002114119,"about_ca_system_score_gemma":0.002526978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02429184,"about_ca_topic_score_gemma":0.03297921,"domain_scores_codex":[0.9988649,0.0001530189,0.0001347726,0.0003383508,0.0002613991,0.0002475743],"domain_scores_gemma":[0.9969186,0.0007704914,0.0004944988,0.0008838712,0.0005423398,0.0003901914],"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":[0.00003953919,0.00002534075,0.0008878041,0.0003384341,0.00001949576,0.00001008113,0.00001324006,0.0001870977,0.000026894,0.0005161382,0.9965953,0.001340658],"study_design_scores_gemma":[0.0005041828,0.0000281325,0.005525219,0.0005094405,0.00003130101,0.00006228262,0.0001002483,0.0008188376,0.0002468537,0.002774273,0.9893646,0.0000346306],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001059453,0.0000611135,0.0000239063,0.00006322646,0.00001953499,0.000004117319,0.9989004,0.000266493,0.0005552066],"genre_scores_gemma":[0.000771055,0.00009514591,0.0001460638,0.00006407727,0.00002181554,0.00004573709,0.9976953,0.0001097437,0.001051115],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7945799,"threshold_uncertainty_score":0.6871989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02912828985409627,"score_gpt":0.2172705724274692,"score_spread":0.188142282573373,"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."}}