{"id":"W3123875691","doi":"","title":"Probabilistic Coherence Weighting for Optimizing Expert Forecasts","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Weighting; Coherence (philosophical gambling strategy); Probabilistic logic; Computer science; Econometrics; Machine learning; Artificial intelligence; Statistics; Economics; Mathematics; Physics","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":[0.004483528,0.0009199971,0.001548561,0.001096053,0.0003763011,0.001288897,0.001337611,0.001627553,0.001991542],"category_scores_gemma":[0.02037717,0.0008988613,0.0007171107,0.001597725,0.0007248213,0.002666224,0.001717925,0.001423097,0.0003408762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006760693,"about_ca_system_score_gemma":0.0009467008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00286445,"about_ca_topic_score_gemma":0.002728284,"domain_scores_codex":[0.9982534,0.0008734893,0.0001167119,0.0001936074,0.0004589253,0.0001038192],"domain_scores_gemma":[0.9923344,0.005962146,0.0003759102,0.0004492284,0.0007605132,0.0001176941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001693602,0.00005724119,0.000470646,0.00009323453,0.00009723163,0.00002922813,0.00006714195,0.8663844,0.001683921,0.02377195,0.001441266,0.1057345],"study_design_scores_gemma":[0.000007176198,0.00001374872,0.00007948916,0.000003096193,0.000006950987,0.000003113581,0.000002358827,0.9940479,0.0002263544,0.005484425,0.0001215622,0.000003927086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009942174,0.0002074963,0.9890965,0.00009532115,0.00001895638,0.00001317243,0.0000280507,0.00009988399,0.0004985046],"genre_scores_gemma":[0.5324425,0.0004753098,0.4634508,0.0001514093,0.0001858401,0.0001950097,0.0004530002,0.0002441577,0.00240204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004483528,"threshold_uncertainty_score":0.02371138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07884684080600221,"score_gpt":0.3611960298727797,"score_spread":0.2823491890667774,"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."}}