{"id":"W2770123796","doi":"10.1016/j.jece.2017.11.068","title":"Photocatalysis with easily recoverable linear engineered TiO2 nanomaterials to prevent the formation of disinfection byproducts in drinking water","year":2017,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta","keywords":"Nanomaterials; Natural organic matter; Photocatalysis; Water treatment; Alkalinity; Environmental chemistry; Degradation (telecommunications); Chemistry; Filtration (mathematics); Surface water; Organic matter; Environmental science; Materials science; Environmental engineering; Nanotechnology; Catalysis; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002347912,0.000127621,0.0001800568,0.0000493531,0.00006261258,0.0000335988,0.0001643331,0.00003984749,0.0001105574],"category_scores_gemma":[0.00002543828,0.00007567991,0.00006269923,0.00004238982,0.0000326317,0.0005798676,0.00009161072,0.00008702566,0.00001858125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002799512,"about_ca_system_score_gemma":0.000001964214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002733249,"about_ca_topic_score_gemma":0.000002880041,"domain_scores_codex":[0.9991517,0.00001175079,0.0003245548,0.0001175383,0.0002301235,0.0001643068],"domain_scores_gemma":[0.9995519,0.00001379211,0.0001677363,0.0002062959,0.000003120806,0.00005718853],"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":[0.00006306997,0.0001275823,0.003983945,0.00001123556,0.00001786147,0.000003769165,0.0002909126,0.05945774,0.9356592,3.614396e-7,0.000006404414,0.0003778625],"study_design_scores_gemma":[0.0003835831,0.0001132542,0.0230813,0.00005672401,0.00003438896,0.00003592557,0.00001254411,0.001243097,0.9747508,0.00001237486,0.0001762536,0.00009976743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989093,0.00001107902,0.0006402935,0.00006884491,0.0001008376,0.0001861204,0.00000228221,0.000004872414,0.00007635196],"genre_scores_gemma":[0.9990801,0.00001054555,0.0007688515,0.000005718513,0.00005699504,0.00000957772,0.000007002311,0.00001289571,0.00004830103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05821464,"threshold_uncertainty_score":0.3086137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004740093718862907,"score_gpt":0.1797354086595814,"score_spread":0.1749953149407185,"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."}}