{"id":"W7056608402","doi":"","title":"Étude comparative des banques de données de mesures d’exposition IMIS (OSHA) et LIMS (IRSST)","year":2018,"lang":"fr","type":"article","venue":"","topic":"Laser Design and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Indemnity; Management information systems; Administration (probate law)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01617627,0.0005941273,0.0009117645,0.006041199,0.001547391,0.002991853,0.001402878,0.0009109883,0.004607914],"category_scores_gemma":[0.04992274,0.0006536598,0.001396272,0.009045911,0.001323758,0.001749043,0.001676289,0.0008728511,0.0008734114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004835448,"about_ca_system_score_gemma":0.005600874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2026822,"about_ca_topic_score_gemma":0.2580063,"domain_scores_codex":[0.9735631,0.005510102,0.002411349,0.002423547,0.01527848,0.0008135388],"domain_scores_gemma":[0.8920841,0.04776835,0.009481398,0.00579976,0.04424099,0.0006253794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003542933,0.0003481588,0.6468859,0.007269769,0.00247117,0.001068133,0.02966516,0.002949541,0.03364515,0.004206144,0.009288201,0.2586599],"study_design_scores_gemma":[0.00004388078,0.000452628,0.8873534,0.001168764,0.0006671385,0.000488512,0.008939602,0.001561314,0.01351412,0.000502622,0.08518988,0.0001179982],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9302928,0.01025055,0.01043256,0.001223243,0.000232828,0.0005472586,0.01903153,0.0002909141,0.02769837],"genre_scores_gemma":[0.9364707,0.005572721,0.01517946,0.0006997145,0.0001215939,0.001123277,0.01752119,0.0001531712,0.0231581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2026822,"threshold_uncertainty_score":0.403005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05169630335192823,"score_gpt":0.3205238035046301,"score_spread":0.2688275001527019,"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."}}