{"id":"W4398540045","doi":"10.7910/dvn/mjkdys/lsgav5","title":"Replication_PSRM_2019.do","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Replication (statistics); Test (biology); Computer science; Econometrics; Operations research; Economics; Statistics; Biology; Engineering; Mathematics; Ecology","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":[0.002155007,0.0003161687,0.0004868032,0.0004173455,0.0001700967,0.0005650593,0.003810341,0.000370889,0.08183947],"category_scores_gemma":[0.002980021,0.0002539565,0.0002175617,0.0007374565,0.0001371766,0.00029948,0.001142552,0.0004672421,0.7397645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006411162,"about_ca_system_score_gemma":0.000175844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001251241,"about_ca_topic_score_gemma":0.00001454575,"domain_scores_codex":[0.9959339,0.0001234808,0.000909632,0.001261143,0.001447316,0.0003245383],"domain_scores_gemma":[0.9898128,0.0006778981,0.0008309906,0.008113458,0.0003981463,0.0001666964],"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":[0.000007251549,0.00004528361,0.000006642801,0.000009655428,0.00001099593,0.000003503236,0.000004817308,0.000004975046,0.000007605763,0.0005946544,0.997053,0.002251574],"study_design_scores_gemma":[0.0001048525,0.00003039092,0.0000313617,0.00003356592,0.00003311042,0.00001696036,0.00002515059,0.00008523567,0.00001316077,0.002855866,0.9964725,0.0002978399],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004279434,0.000001091105,0.001011513,0.00004221497,0.000493626,0.0005380656,0.9937177,0.0001035007,0.00408802],"genre_scores_gemma":[0.00001984357,0.0000887702,0.002324609,0.0006047001,0.0002074574,0.00009724738,0.9899844,0.0000226865,0.006650284],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.657925,"threshold_uncertainty_score":0.9999912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1322931755977421,"score_gpt":0.3925640271343547,"score_spread":0.2602708515366127,"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."}}