{"id":"W4240745220","doi":"10.32920/ryerson.14643972","title":"Photochemical treatment of organic constituents and bacterial pathogens from synthetic slaughterhouse wastewater by combining vacuum-UV and UV-C","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultraviolet; Wastewater; Chemistry; Degradation (telecommunications); Total organic carbon; Irradiation; Molar ratio; Bacteria; Photochemistry; Nuclear chemistry; Environmental chemistry; Materials science; Organic chemistry; Environmental engineering; Catalysis; Environmental science; Biology; Optoelectronics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000940784,0.0003366456,0.0006174161,0.00002644399,0.00006927161,0.0001265959,0.0001437698,0.0002237351,0.001607606],"category_scores_gemma":[0.00001145557,0.0002676095,0.0001100723,0.00004759557,0.0002826682,0.00005511158,0.0007669017,0.0001231627,0.00001567368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001229733,"about_ca_system_score_gemma":0.00001469114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002477926,"about_ca_topic_score_gemma":0.00002859502,"domain_scores_codex":[0.9982078,0.0001323463,0.0004107047,0.0007656045,0.0002441178,0.0002394363],"domain_scores_gemma":[0.999166,0.0000762323,0.0001361045,0.0004426316,0.000008356317,0.00017069],"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":[0.00002655595,0.0002447146,0.03092162,0.00002775697,0.0002056958,0.00001721188,0.001560645,0.000003626407,0.9666441,2.883527e-7,0.00005872495,0.0002890266],"study_design_scores_gemma":[0.0007912672,0.0000744166,0.001563198,0.000126709,0.0004101927,0.000006535542,0.0004398519,0.00009891522,0.9958693,0.00006751633,0.0001715099,0.000380538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989982,0.0001420866,0.00004248184,0.00007039847,0.0002516795,0.0001845533,0.0002187021,0.00003323862,0.00005864467],"genre_scores_gemma":[0.9979337,0.0002649734,0.001117847,0.000008595507,0.0000571671,0.00002324427,0.0002827359,0.00002382738,0.0002878948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02935842,"threshold_uncertainty_score":0.9999776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153733627437993,"score_gpt":0.2252727163621699,"score_spread":0.2098993536183706,"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."}}