{"id":"W4400542653","doi":"10.1139/cjc-2024-0013","title":"Profiling organic pollutants in environmental water by dansylation-based non-targeted liquid chromatography-high resolution mass spectrometry analysis","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Chemistry; Mass spectrometry; Chromatography; Pollutant; Profiling (computer programming); Resolution (logic); Environmental analysis; Environmental chemistry; Organic chemistry; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004932755,0.0003368734,0.0005067163,0.000427678,0.0001063414,0.0001163744,0.0004143742,0.0003342002,0.01072431],"category_scores_gemma":[0.00009800446,0.0003126966,0.0003395049,0.001011208,0.0001544589,0.0001412231,0.00001901407,0.0007672888,0.00001513868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628279,"about_ca_system_score_gemma":0.001022596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001406424,"about_ca_topic_score_gemma":0.00008606112,"domain_scores_codex":[0.9975036,0.00002827272,0.0008875009,0.0004352737,0.0004583175,0.0006870796],"domain_scores_gemma":[0.9986183,0.00008579949,0.000171119,0.000316184,0.00004593138,0.0007626358],"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":[0.00004040976,0.00002621926,0.00795246,0.0002248855,0.0005471966,0.0004532567,0.00008003262,0.000231254,0.9903114,5.961613e-7,0.0000797734,0.0000525037],"study_design_scores_gemma":[0.0003951033,0.00001569155,0.0004694799,0.0001629162,0.0002926848,0.00005000006,0.0001976783,0.002606268,0.9950969,0.00005467245,0.0002960835,0.0003625381],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870527,0.001214004,0.009925408,0.0003190771,0.00008055817,0.00004345827,0.0002806802,0.00002494551,0.001059127],"genre_scores_gemma":[0.994147,0.00002079615,0.004833961,0.00003543598,0.0001755894,0.000004015957,0.0002814399,0.00005154495,0.0004502109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01070917,"threshold_uncertainty_score":0.9999325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00527587389282481,"score_gpt":0.2080090125424395,"score_spread":0.2027331386496147,"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."}}