{"id":"W2042110235","doi":"10.1002/env.1055","title":"Latent health factor index: a statistical modeling approach for ecological health assessment","year":2010,"lang":"en","type":"article","venue":"Environmetrics","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information; University of Saskatchewan; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Commonwealth Scientific and Industrial Research Organisation","keywords":"Computer science; Unobservable; Econometrics; Covariance; Index of biological integrity; Bayesian probability; Statistical inference; Identifiability; Markov chain Monte Carlo; Data mining; Statistics; Ecology; Mathematics; Machine learning; Artificial intelligence","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":[0.001524581,0.0002152965,0.0003195933,0.00008281753,0.0006073433,0.00007975077,0.0002697962,0.0001500075,0.0006702944],"category_scores_gemma":[0.0001127755,0.0001869766,0.00008795333,0.0002553863,0.0001651119,0.0001197784,0.0002008862,0.0005194818,0.0000844165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007698231,"about_ca_system_score_gemma":0.00009492099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003075582,"about_ca_topic_score_gemma":0.0001004655,"domain_scores_codex":[0.9973335,0.0001893752,0.000586033,0.0005454086,0.0005881536,0.0007575515],"domain_scores_gemma":[0.9986602,0.0002430719,0.0002024869,0.0003295409,0.000004501577,0.0005602131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001631275,0.006930334,0.3019397,0.0002528536,0.0001218788,0.00000792097,0.001311176,0.1957895,0.001051155,0.04429832,0.01873493,0.4293991],"study_design_scores_gemma":[0.0008586717,0.0006131581,0.3791111,0.000002384585,0.000005439879,0.000005111627,0.00007237866,0.5636871,0.00001671121,0.001607929,0.05363217,0.0003878353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07527672,0.00003383197,0.9209369,0.001884214,0.0003231861,0.0007687362,0.0002098286,0.00006166446,0.0005048587],"genre_scores_gemma":[0.7941324,0.000107179,0.2043847,0.0008344757,0.00005827494,0.00008312518,0.0001062843,0.00001941381,0.0002741359],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7188557,"threshold_uncertainty_score":0.7624686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07301303280434468,"score_gpt":0.3486024161303826,"score_spread":0.2755893833260379,"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."}}