{"id":"W7117103069","doi":"10.1016/j.jhazmat.2025.140934","title":"Towards smart PFAS management: Integrating artificial intelligence in water and wastewater systems","year":2025,"lang":"en","type":"article","venue":"Journal of Hazardous Materials","topic":"Per- and polyfluoroalkyl substances research","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Commercializations Promotion Agency for R and D Outcomes; Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Interpretability; Generative grammar; Artificial neural network; Classifier (UML); Graph; Transfer of learning; Predictive modelling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036368,0.0008094878,0.001056617,0.0005618717,0.000370155,0.003296217,0.001081782,0.001524139,0.001364111],"category_scores_gemma":[0.001169086,0.0002899789,0.0007238252,0.0005127566,0.0008034644,0.003202378,0.001612558,0.001514813,0.0004548079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006902809,"about_ca_system_score_gemma":0.001094966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683369,"about_ca_topic_score_gemma":0.002168009,"domain_scores_codex":[0.9995016,0.0001150636,0.00003860568,0.0001099057,0.0001691886,0.00006560287],"domain_scores_gemma":[0.9995009,0.0001477132,0.0000873676,0.00006240381,0.0001644629,0.00003724539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003228702,0.000792098,0.006504046,0.0008898722,0.0004521717,0.0002091897,0.0002206064,0.4423017,0.06368873,0.03787665,0.005890549,0.4408514],"study_design_scores_gemma":[0.00002393796,0.0002242921,0.001356556,0.00007600043,0.00008687835,0.00005832047,0.0002098563,0.9276438,0.01445375,0.04477002,0.0110519,0.00004473352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09201351,0.004334075,0.8781884,0.00403297,0.0002915673,0.0001689027,0.0002160245,0.001402085,0.01935231],"genre_scores_gemma":[0.8412686,0.003064516,0.1501572,0.0008721967,0.0001653477,0.00009473765,0.0002968217,0.00006182223,0.004018739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003296217,"threshold_uncertainty_score":0.005480886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676247536899672,"score_gpt":0.2781317155792384,"score_spread":0.2613692402102417,"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."}}