{"id":"W2890678350","doi":"10.23889/ijpds.v3i4.1011","title":"Data linkage to build detailed return-to-work trajectories for work disability research","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Workers' compensation; Workforce; Work (physics); Context (archaeology); Population; Business; Compensation (psychology); Medicine; Actuarial science; Psychology; Environmental health; Economics; Engineering; Economic growth","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":["metaresearch","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.02257246,0.0001547468,0.000229773,0.0005208767,0.004350053,0.0002971061,0.006742472,0.0001261909,0.0002947228],"category_scores_gemma":[0.03342756,0.0001306285,0.0000458425,0.001977736,0.0004708343,0.002214693,0.002512962,0.0007902791,0.0002195863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009306781,"about_ca_system_score_gemma":0.00163623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003864017,"about_ca_topic_score_gemma":0.002162145,"domain_scores_codex":[0.9936622,0.0004198132,0.001082539,0.001016855,0.00270846,0.001110099],"domain_scores_gemma":[0.9875914,0.003790567,0.0002647128,0.001825882,0.005641542,0.0008858484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004071601,0.0001449038,0.8579181,0.00006845703,0.00002808483,0.000001488294,0.0007647643,0.00005009804,0.0001511242,0.004524502,0.09197854,0.04029831],"study_design_scores_gemma":[0.000661757,0.0002727772,0.7507851,0.0002996748,0.000006280265,0.00000303025,0.0002514119,0.003353948,0.00001766119,0.003460586,0.2407205,0.000167309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7917911,0.00005678771,0.09479031,0.07078525,0.021342,0.007744658,0.01272869,0.0001004226,0.0006607683],"genre_scores_gemma":[0.9435714,0.00001374194,0.04486953,0.001012331,0.007072614,0.0002470506,0.002389324,0.00002732719,0.0007967196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1517802,"threshold_uncertainty_score":0.9986315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4821570186123421,"score_gpt":0.6399370753878652,"score_spread":0.1577800567755231,"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."}}