{"id":"W4398914843","doi":"10.7910/dvn/tpzpio/dvgkgu","title":"posterior_anes_2000.rds","year":2020,"lang":"es","type":"dataset","venue":"Harvard Dataverse","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Ideology; Political science; Psychology; Computer science; Data science; Medicine; Virology; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.00237038,0.002490536,0.001706504,0.005219874,0.00085286,0.004008821,0.00383693,0.002863462,0.1905081],"category_scores_gemma":[0.01536195,0.001223366,0.00205472,0.00821693,0.0006842548,0.001762778,0.002180689,0.002492499,0.1580776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001810604,"about_ca_system_score_gemma":0.002809555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03348067,"about_ca_topic_score_gemma":0.04870067,"domain_scores_codex":[0.9986666,0.0003567742,0.0001355101,0.0003370483,0.0002805711,0.0002234307],"domain_scores_gemma":[0.9956741,0.001569224,0.0004257185,0.001219148,0.0006860174,0.0004257137],"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":[0.00004757827,0.00001348568,0.000598372,0.0003768025,0.00004389771,0.000009482108,0.00001228687,0.0002944637,0.0000247626,0.0004646555,0.9967895,0.001324673],"study_design_scores_gemma":[0.0007266421,0.00002799654,0.004666363,0.0004188875,0.00007552723,0.00005925228,0.00006285131,0.001077121,0.0002833591,0.002994693,0.9895636,0.00004370564],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000735544,0.00004320541,0.00005523495,0.00006645219,0.00002239175,0.000004799616,0.9988977,0.0003642504,0.0004723743],"genre_scores_gemma":[0.0006046035,0.00006585325,0.000230787,0.00006419062,0.00002041199,0.0000711605,0.9978144,0.0001532556,0.0009753443],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1905081,"threshold_uncertainty_score":0.6373132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03975243141920166,"score_gpt":0.3253758615186852,"score_spread":0.2856234300994836,"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."}}