{"id":"W227407425","doi":"10.1016/j.chemosphere.2015.05.034","title":"Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling","year":2015,"lang":"en","type":"article","venue":"Chemosphere","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; University of Waterloo; Università degli Studi di Milano-Bicocca","keywords":"Quantitative structure–activity relationship; Applicability domain; Molecular descriptor; Chemistry; Outlier; Linear regression; Training set; Hydroxyl radical; Biological system; Biochemical engineering; Radical; Computer science; Machine learning; Artificial intelligence; Organic chemistry; Stereochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0003742184,0.0001213217,0.0001453448,0.000004651248,0.00005854569,0.00001300662,0.0001338843,0.00005081512,0.0002329415],"category_scores_gemma":[0.00002247236,0.00006564396,0.00002250873,0.0001069613,0.0003322376,0.0002044235,0.0001185686,0.0001515277,0.00004828945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001699218,"about_ca_system_score_gemma":0.00001186816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000501041,"about_ca_topic_score_gemma":0.00003557586,"domain_scores_codex":[0.9990191,0.00005224222,0.00021297,0.000199152,0.000219747,0.0002967898],"domain_scores_gemma":[0.9996408,0.00001920793,0.00005384376,0.00014977,0.000003026276,0.0001334015],"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":[0.00009082644,0.00008775072,0.04650591,0.000009527853,0.000009806424,0.00001441206,0.000505088,0.02620496,0.9255447,0.000001216124,0.0000196262,0.001006198],"study_design_scores_gemma":[0.0009971805,0.00004281641,0.002230589,0.00009738224,0.00002529432,0.00004275805,0.0009610581,0.229806,0.7653492,0.00017925,0.0001121967,0.0001562874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962179,0.00004689955,0.000243534,0.0002131723,0.00004141788,0.0001529121,0.00000280221,0.00001263589,0.003068737],"genre_scores_gemma":[0.9991938,0.00001538355,0.0005043417,0.0001804383,0.00001689938,7.520411e-7,0.000001398655,0.00001332048,0.00007361051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.203601,"threshold_uncertainty_score":0.2676883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04546421989018024,"score_gpt":0.2715842710165475,"score_spread":0.2261200511263672,"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."}}