{"id":"W2057919842","doi":"10.1097/00001648-200611001-01270","title":"Continuing Advances in Acute Dietary Risk Assessment for Agrochemicals","year":2006,"lang":"en","type":"article","venue":"Epidemiology","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Probabilistic logic; Agrochemical; Risk assessment; Consumption (sociology); Environmental health; Probabilistic risk assessment; Reference dose; Exposure assessment; Medicine; Toxicology; Risk analysis (engineering); Computer science; Environmental science; Statistics; Agriculture; Mathematics; Biology; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00121972,0.0001185979,0.0004845347,0.00001236516,0.00007482171,0.000005671992,0.0001543117,0.0001130472,0.0000774266],"category_scores_gemma":[0.0005537262,0.00004414457,0.0001759614,0.0001416663,0.00005682566,0.00008904655,0.00003637213,0.0001281821,0.000005578887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002216222,"about_ca_system_score_gemma":0.000003026707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006184448,"about_ca_topic_score_gemma":0.001840209,"domain_scores_codex":[0.998445,0.0002967631,0.0004982896,0.0003253653,0.00004987568,0.0003846588],"domain_scores_gemma":[0.995928,0.003723292,0.0002176821,0.00004956606,0.0000359563,0.00004543273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004248021,0.00007624006,0.8044571,0.000005094473,0.00002740916,0.000004754006,0.000005334259,0.0004049526,0.05458996,0.002454445,0.001548656,0.1363836],"study_design_scores_gemma":[0.0001461659,0.0001033419,0.9250885,0.00001245308,0.00003731982,0.000002097404,0.00001803027,0.001355476,0.0002827046,0.04887619,0.02394566,0.0001320712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913604,0.003461144,0.0007560691,0.002940221,0.00005787937,0.0001676355,0.00006400589,0.00002987225,0.001162726],"genre_scores_gemma":[0.9937105,0.001720561,0.003491512,0.0004007477,0.0002938311,0.00005492359,0.0002100478,6.990879e-7,0.0001171714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1362515,"threshold_uncertainty_score":0.1800164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047619808578617,"score_gpt":0.3176382775822648,"score_spread":0.2971620794964786,"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."}}