{"id":"W2086950964","doi":"10.3386/w16454","title":"Decoding Inside Information","year":2010,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":239,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoding methods; Computer science; Telecommunications","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":[0.001853148,0.001164026,0.001216456,0.001540813,0.0005841422,0.003023948,0.0008845179,0.001267353,0.006044451],"category_scores_gemma":[0.02255175,0.0004924346,0.0007031813,0.001443788,0.00105329,0.004395839,0.001699774,0.001751732,0.001650807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000681055,"about_ca_system_score_gemma":0.0009434839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001854136,"about_ca_topic_score_gemma":0.001732923,"domain_scores_codex":[0.9987416,0.0003362975,0.00007113979,0.0003201805,0.0003655119,0.0001652439],"domain_scores_gemma":[0.9919884,0.00453504,0.0008571048,0.001825453,0.0006120985,0.0001819755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007819652,0.0002857643,0.0405745,0.0002642981,0.0003038951,0.0008875444,0.0008501035,0.1110144,0.01382778,0.4449332,0.01710287,0.3691737],"study_design_scores_gemma":[0.00004739101,0.0001233794,0.009602423,0.0000941684,0.00008905489,0.000445688,0.0002189097,0.509595,0.01320936,0.4543399,0.01214715,0.00008746693],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2475683,0.0009298695,0.7044494,0.003319288,0.0003729299,0.0001247749,0.003738103,0.001193974,0.03830345],"genre_scores_gemma":[0.926571,0.0006155582,0.05935824,0.0004475029,0.000332051,0.00008327284,0.002349251,0.0001763673,0.01006688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006044451,"threshold_uncertainty_score":0.0202207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3815532230493952,"score_gpt":0.455063875115755,"score_spread":0.07351065206635987,"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."}}