{"id":"W4390871517","doi":"10.1039/d3ew00922j","title":"Evidence-based framework to use <i>in situ</i> phycocyanin readings for cyanobacterial risk assessment within drinking water treatment plants","year":2024,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Génome Québec; Genome Canada","keywords":"Phycocyanin; In situ; Water treatment; Environmental science; Water source; Environmental chemistry; Environmental engineering; Water resource management; Hydrology (agriculture); Cyanobacteria; Chemistry; Biology; Engineering; Geotechnical engineering","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":[0.1072831,0.003652553,0.004664724,0.0211061,0.002684914,0.009148613,0.009763575,0.01002866,0.0033781],"category_scores_gemma":[0.1334255,0.001232272,0.006325121,0.00702452,0.006210409,0.005598222,0.00631009,0.00742354,0.0008009548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01142407,"about_ca_system_score_gemma":0.0263475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02001595,"about_ca_topic_score_gemma":0.0210226,"domain_scores_codex":[0.9293211,0.04629248,0.01064589,0.004689697,0.008083855,0.0009669442],"domain_scores_gemma":[0.8303844,0.1355013,0.01070573,0.003376662,0.01818937,0.001842638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007500324,0.001070093,0.02734094,0.03817825,0.009153886,0.003304689,0.002309856,0.112073,0.001263913,0.4372603,0.03196836,0.3353267],"study_design_scores_gemma":[0.00104641,0.00155145,0.01019877,0.04972785,0.009265558,0.00205513,0.002449009,0.09324042,0.001870978,0.7379621,0.0901159,0.0005164075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00809298,0.04475639,0.7567301,0.1512655,0.001512349,0.006184066,0.004591411,0.0005195808,0.02634762],"genre_scores_gemma":[0.2458941,0.01243908,0.714983,0.0160999,0.0009499798,0.006834097,0.001446433,0.00003187195,0.001321605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1072831,"threshold_uncertainty_score":0.5673739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04534137614433398,"score_gpt":0.3249315655809348,"score_spread":0.2795901894366008,"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."}}