{"id":"W2023879359","doi":"10.1126/science.1062633","title":"Harnessing Genomics and Biotechnology to Improve Global Health Equity","year":2001,"lang":"en","type":"letter","venue":"Science","topic":"Biotechnology and Related Fields","field":"Medicine","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Equity (law); Action plan; Genomics; Global health; Developing country; Business; Biotechnology; Economic growth; Health equity; Political science; Genome; Health care; Biology; Economics; Genetics; Management; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0004579866,0.0001926792,0.0003765543,0.0002834226,0.0003280426,0.00004059591,0.0004376516,0.005897217,0.00001355817],"category_scores_gemma":[0.0001270234,0.0001579072,0.00003661009,0.0009980092,0.001780235,0.00005145995,0.0006360266,0.005187605,0.00004728116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005347556,"about_ca_system_score_gemma":0.001207892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006861653,"about_ca_topic_score_gemma":0.00001025263,"domain_scores_codex":[0.998037,0.0000125127,0.0002252457,0.0007407792,0.0002505153,0.0007339159],"domain_scores_gemma":[0.9991181,0.00001174085,0.0001172677,0.0005511143,0.0000528818,0.0001488481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003538491,0.00004189353,0.0002418082,0.0002649025,0.00004440101,0.0007284723,0.0001102575,8.094004e-7,0.01883475,0.001869777,0.3922806,0.5855469],"study_design_scores_gemma":[0.0002710231,0.0004236797,0.0006749812,0.0001659025,0.00002970705,0.001037984,0.0000300684,0.00005540745,0.002897546,0.001323644,0.9928762,0.0002138769],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.006405451,0.001527754,0.001551379,0.9875504,0.0009684307,0.0003509604,0.00001613081,0.0001892659,0.001440234],"genre_scores_gemma":[0.007059705,0.00143471,0.004742383,0.9851897,0.0006000798,0.000006723182,0.000009474331,0.0000148926,0.0009422769],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.6005956,"threshold_uncertainty_score":0.9971074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910686408977638,"score_gpt":0.3344653411614051,"score_spread":0.3153584770716287,"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."}}