{"id":"W4398682233","doi":"10.7910/dvn/dhwzye/rwvyqp","title":"output_appendix.txt","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Criminal Justice and Corrections Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Depression (economics); Appendix; Psychology; Great Depression; Political science; Economics; Law; Keynesian economics; Geology","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":["insufficient_payload"],"category_scores_codex":[0.00202067,0.00183679,0.00138225,0.003053298,0.0007897952,0.0032921,0.002097273,0.001097955,0.4666123],"category_scores_gemma":[0.01496387,0.001214658,0.001299662,0.004492262,0.0004674379,0.001653535,0.002530175,0.001617039,0.3636854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419503,"about_ca_system_score_gemma":0.002213119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01164213,"about_ca_topic_score_gemma":0.01704628,"domain_scores_codex":[0.9987006,0.0002499521,0.0001995852,0.0004447187,0.0001995443,0.000205583],"domain_scores_gemma":[0.9937713,0.002668658,0.0005190629,0.001280887,0.001356964,0.0004031398],"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":[0.00004744574,0.00001025388,0.0005218233,0.00037188,0.00002312729,0.000007930128,0.00001476523,0.00007634993,0.00004642842,0.0002737058,0.9968534,0.001752936],"study_design_scores_gemma":[0.0005275222,0.00003048425,0.005594026,0.0003170137,0.00005580505,0.00004551359,0.00008584372,0.0004150587,0.0006545851,0.003595432,0.9886323,0.00004646254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005672549,0.00001133729,0.0001204563,0.00005893986,0.00001792359,0.00001636986,0.998099,0.0008424143,0.0007769477],"genre_scores_gemma":[0.0008233524,0.00004353943,0.0008474913,0.000145207,0.00002497821,0.0003483865,0.9936022,0.001259217,0.002905621],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5333877,"threshold_uncertainty_score":0.7608128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03376281002659214,"score_gpt":0.3139514788181929,"score_spread":0.2801886687916008,"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."}}