{"id":"W2015368076","doi":"10.3390/ijerph10041231","title":"Management of Occupational Exposure to Engineered Nanoparticles Through a Chance-Constrained Nonlinear Programming Approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Nonlinear programming; Production (economics); Occupational safety and health; Risk analysis (engineering); Computer science; Process (computing); Control (management); Nonlinear system; Business; Medicine; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001865944,0.001298871,0.001070246,0.0007188093,0.0004753423,0.001325441,0.001148732,0.001434044,0.001898134],"category_scores_gemma":[0.002917456,0.0008129412,0.001143413,0.0005209923,0.0007972418,0.0009818602,0.001509754,0.001141231,0.0001381351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073542,"about_ca_system_score_gemma":0.002127218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006438435,"about_ca_topic_score_gemma":0.004892857,"domain_scores_codex":[0.9991492,0.0003764605,0.00003369982,0.0001534073,0.0001833443,0.0001039484],"domain_scores_gemma":[0.9978042,0.001623231,0.0002437811,0.00003658903,0.0002140551,0.00007806977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001898533,0.0000230828,0.0003006338,0.00003546477,0.00002333424,0.00004383798,0.00001723239,0.9914097,0.000431243,0.00313891,0.0001317542,0.004425936],"study_design_scores_gemma":[0.000005693067,0.00003519263,0.00008615031,0.000005546579,0.000007718904,0.000007531967,0.000009769984,0.9965889,0.0001921373,0.002858252,0.0001974469,0.000005624363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02787215,0.0003116928,0.966604,0.0005222362,0.00003570654,0.0000872711,0.00009758177,0.00006357947,0.004405844],"genre_scores_gemma":[0.7828258,0.000607335,0.2092379,0.0002554569,0.00007717453,0.0005487893,0.0001790194,0.00006995488,0.006198629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006438435,"threshold_uncertainty_score":0.01280195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07951814854444647,"score_gpt":0.3612611448863915,"score_spread":0.2817429963419451,"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."}}