{"id":"W2981983464","doi":"10.1037/e533542011-001","title":"Project Retrosight: Understanding the Returns from Cardiovascular and Stroke Research","year":2011,"lang":"en","type":"dataset","venue":"PsycEXTRA Dataset","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stroke (engine); Engineering; Aerospace engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008820467,0.0004470048,0.0009674308,0.000532573,0.0005387747,0.0001641423,0.0008297631,0.0007154717,0.0008536027],"category_scores_gemma":[0.01739271,0.0002706391,0.0002212383,0.0005734331,0.001025866,0.0001453387,0.0005192356,0.004540669,0.0003033719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005436763,"about_ca_system_score_gemma":0.004463539,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0126328,"about_ca_topic_score_gemma":0.0008665833,"domain_scores_codex":[0.9918508,0.001114471,0.0005743753,0.001063544,0.003366735,0.002030107],"domain_scores_gemma":[0.9915556,0.001273364,0.0001162287,0.003368112,0.0001523838,0.003534341],"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":[0.0004579553,0.0001184018,0.00002012932,0.0007180768,0.000646774,0.0006192227,0.0001191207,1.067826e-8,0.00001216478,0.00001607728,0.9964018,0.0008703088],"study_design_scores_gemma":[0.001454209,0.0006151924,0.0001041504,0.0006683044,0.0005133972,0.0001246311,0.0005890601,0.000005073979,0.00001343323,0.0002604743,0.9954033,0.000248718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000479028,0.007261193,0.00005191977,0.002855924,0.0003449957,0.002077499,0.9870694,0.00002850165,0.0002627101],"genre_scores_gemma":[0.0001615615,0.01795536,0.0001003069,0.002100266,0.001176583,0.0001277766,0.978251,0.000044436,0.00008270849],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01176621,"threshold_uncertainty_score":0.9999746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.610050545839668,"score_gpt":0.5016470226635397,"score_spread":0.1084035231761283,"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."}}