{"id":"W4399012760","doi":"10.7910/dvn/zbrtjh/t9tlsn","title":"Figure2LoadModules.R","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Replication (statistics); Inference; Root (linguistics); Computer science; Statistics; Mathematics; Artificial intelligence; Philosophy; Linguistics","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":[0.0002766222,0.0003898802,0.0004027678,0.0001796242,0.00009288419,0.0004016762,0.00383912,0.0003789875,0.004988362],"category_scores_gemma":[0.0000840023,0.0003748874,0.0001338585,0.0002314998,0.00005030875,0.000638641,0.001297006,0.0006330454,0.4517133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006676313,"about_ca_system_score_gemma":0.0002920782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000110129,"about_ca_topic_score_gemma":0.00001531464,"domain_scores_codex":[0.9976891,0.00008756314,0.0003286703,0.0009182304,0.000504039,0.0004724249],"domain_scores_gemma":[0.995641,0.00006866098,0.0001950552,0.003817581,0.00009289016,0.0001848355],"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":[0.000003190811,0.00004215899,4.153575e-7,0.00006831297,0.00002656467,0.00005415471,0.000008599554,0.00003716014,0.000004233593,0.0009649321,0.9977916,0.0009986465],"study_design_scores_gemma":[0.00018808,0.00004739312,0.00000377837,0.00009555408,0.00002833628,0.0000225898,0.000002338014,0.004485282,0.00001491769,0.0003345884,0.9943148,0.0004622688],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[5.865208e-7,0.00000259955,0.09955898,0.0000160193,0.001499985,0.0001552016,0.8982928,0.0001195642,0.0003542848],"genre_scores_gemma":[0.00001135671,0.0002120901,0.004602726,0.001005145,0.0001959105,0.00001464895,0.9931701,0.00001407282,0.0007739763],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4467249,"threshold_uncertainty_score":0.9998703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961951228597605,"score_gpt":0.2564553709340389,"score_spread":0.2268358586480628,"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."}}