{"id":"W2904464026","doi":"10.1039/c8ew00821c","title":"Fluorescence excitation emission matrices for rapid detection of polycyclic aromatic hydrocarbons and pesticides in surface waters","year":2018,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo; Okanagan University College; Toronto Public Health; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fluorescence; Environmental chemistry; Pesticide; Polycyclic aromatic hydrocarbon; Environmental science; Water quality; Chemistry; Surface water; Environmental engineering; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002028169,0.0001266254,0.0001654177,0.0004674265,0.0004453912,0.00003998132,0.0004659565,0.00009902177,0.00001908798],"category_scores_gemma":[0.0001135507,0.00009320606,0.00002761784,0.0008434479,0.004559934,0.0003569565,0.0004375662,0.0001731378,0.00005461158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178825,"about_ca_system_score_gemma":0.000007576509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005576801,"about_ca_topic_score_gemma":0.00001356616,"domain_scores_codex":[0.9977326,0.0001060849,0.0002930241,0.0005540705,0.0006439745,0.0006701985],"domain_scores_gemma":[0.9994833,0.00006416692,0.00005661183,0.0002866578,0.000002363527,0.0001068677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002181057,0.00006009271,0.04259339,0.000005923944,0.000002581711,0.000001611975,0.0009498195,0.00001185343,0.9475058,0.000001515496,0.00000247912,0.008843119],"study_design_scores_gemma":[0.0001977621,0.000397535,0.03487419,0.00002799578,0.000005717821,0.00000672855,0.001071987,0.002583358,0.9564926,0.004157794,0.0000677206,0.0001166011],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988296,0.00003627437,0.0002118788,0.0005157007,0.00004256114,0.0002940439,0.000001365618,0.00003104936,0.00003751027],"genre_scores_gemma":[0.9984297,0.00004348551,0.001377072,0.000003385636,0.0000162839,0.00003233984,0.000002236887,0.000009689571,0.000085766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008986811,"threshold_uncertainty_score":0.9981491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399199324562982,"score_gpt":0.3035793716212626,"score_spread":0.2795873783756328,"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."}}