{"id":"W2898523488","doi":"10.1007/s00244-018-0576-0","title":"Investigation of Spatial Distributions and Temporal Trends of Triclosan in Canadian Surface Waters","year":2018,"lang":"en","type":"article","venue":"Archives of Environmental Contamination and Toxicology","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Triclosan; Environmental science; Environmental chemistry; Wastewater; Aquatic ecosystem; Triclocarban; Ecotoxicology; Toxicology; Chemistry; Environmental engineering; Biology; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00009962947,0.00008414789,0.0001521092,0.00008349062,0.00003887392,0.000001916346,0.00005902617,0.00004526991,0.0003438182],"category_scores_gemma":[0.00001238432,0.00007983451,0.00002034852,0.00006832365,0.001829321,0.00008881019,0.000085273,0.00005275418,0.000002446709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004670457,"about_ca_system_score_gemma":0.00000672639,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01417124,"about_ca_topic_score_gemma":0.01591168,"domain_scores_codex":[0.9992739,0.00007477647,0.0002402432,0.0001571099,0.00008444059,0.0001695463],"domain_scores_gemma":[0.9996173,0.00006338842,0.00009797153,0.00006401588,6.091124e-7,0.0001566961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002256067,0.00004700149,0.6587318,0.000005651324,0.000004599195,8.822373e-7,0.0004059112,0.000002656978,0.3271553,0.000109588,0.000002407578,0.01351161],"study_design_scores_gemma":[0.0005314182,0.0003421931,0.8543211,0.000009159124,0.00001002773,0.000003508543,0.00008733417,0.0007388576,0.1435489,0.0002051643,0.0001407974,0.00006160622],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977957,0.00002189929,0.0000535378,0.000290777,0.00002015723,0.00009262297,0.0001013943,0.000001750481,0.001622175],"genre_scores_gemma":[0.9994665,0.00005965587,0.0002966496,0.00004783829,0.000004324233,8.458381e-7,0.00004114526,0.00000398962,0.0000790906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1955893,"threshold_uncertainty_score":0.9923935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439365076419063,"score_gpt":0.2466281625670922,"score_spread":0.2322345118029016,"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."}}