{"id":"W2792657571","doi":"10.1371/journal.pone.0194997","title":"The author who wasn’t there? Fairness and attribution in publications following access to population biobanks","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill Genome Centre","funders":"Partenariat Canadien Contre Le Cancer","keywords":"Biobank; Documentation; Normative; Context (archaeology); Attribution; Population; Accountability; Political science; Authorship attribution; Citation; Public relations; Engineering ethics; Library science; Psychology; Computer science; Medicine; Law; Engineering; Social psychology; Bioinformatics; Geography; Biology; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4181404,0.0003300388,0.00110635,0.004941246,0.01125207,0.01977,0.002975584,0.004429618,0.002082927],"category_scores_gemma":[0.6799256,0.0006211311,0.0006999859,0.006191514,0.02692466,0.01736175,0.01198356,0.005315663,0.0004625409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009312449,"about_ca_system_score_gemma":0.02314115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570668,"about_ca_topic_score_gemma":0.002365272,"domain_scores_codex":[0.4059397,0.4989815,0.03092907,0.01182065,0.04377032,0.008558843],"domain_scores_gemma":[0.1567168,0.6986547,0.07090914,0.03902176,0.02799319,0.006704426],"domain_codex":"methods","domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003573492,0.00009340504,0.05069607,0.0009537528,0.0001280217,0.001494121,0.5477966,0.0007605398,0.0007762171,0.2664123,0.005162795,0.1253688],"study_design_scores_gemma":[0.000115853,0.0001946626,0.03022284,0.005834552,0.0001867694,0.002028878,0.3130782,0.003520059,0.004126937,0.5199146,0.120439,0.0003378125],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6054626,0.01058946,0.1215842,0.1861096,0.002753089,0.0005568112,0.000222018,0.000182593,0.07253961],"genre_scores_gemma":[0.9840507,0.001002721,0.009088052,0.003259443,0.0004488498,0.0001761636,0.00003182881,0.00005305851,0.001889226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9955704,"threshold_uncertainty_score":0.7175365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.623969504471773,"score_gpt":0.5581428890243005,"score_spread":0.06582661544747248,"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."}}