{"id":"W2510917126","doi":"10.1136/oemed-2016-103951.196","title":"O38-3 Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the interocc study","year":2016,"lang":"en","type":"article","venue":"","topic":"Chemical Safety and Risk Management","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Occupational exposure; Exposure assessment; Electromagnetic field; Job-exposure matrix; Matrix (chemical analysis); Medicine; Computer science; Environmental health; Physics; Materials science; Composite material; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003202497,0.0001094666,0.0001858069,0.00004913383,0.00002098707,0.00000415358,0.0002538992,0.00005625786,0.0001750808],"category_scores_gemma":[0.0000474909,0.00005786931,0.00006728376,0.0001131245,0.0000173163,0.00003043139,0.00007294484,0.00007878557,0.000001300998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005399262,"about_ca_system_score_gemma":0.00003325666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001449301,"about_ca_topic_score_gemma":0.00007308354,"domain_scores_codex":[0.9989125,0.00002306515,0.0004841855,0.000158533,0.0002535993,0.0001681059],"domain_scores_gemma":[0.9993573,0.0003085645,0.00008264305,0.0001833266,0.00004595414,0.00002221444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001026582,0.006112226,0.139034,0.00112296,0.0006214118,0.000004653094,0.01008206,0.001849718,0.7414876,0.01409354,0.001863562,0.08270161],"study_design_scores_gemma":[0.01901774,0.005428792,0.554751,0.0006583576,0.0002768349,0.000005420009,0.007095587,0.01120178,0.3864995,0.001477345,0.01229033,0.001297292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8446352,0.00002739422,0.1541392,0.0002327739,0.00002515449,0.0004843044,0.000003378386,0.00001633447,0.0004361799],"genre_scores_gemma":[0.9916108,0.000004133629,0.007631873,0.00002646809,0.00002436542,0.0001246903,0.000004983458,0.000007924126,0.0005647498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.415717,"threshold_uncertainty_score":0.2359842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371795446621591,"score_gpt":0.290980710172048,"score_spread":0.277262755705832,"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."}}