{"id":"W2005801573","doi":"10.1016/j.ecolmodel.2012.05.023","title":"A Bayesian synthesis of predictions from different models for setting water quality criteria","year":2012,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Bayesian probability; Econometrics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008129832,0.001021394,0.001465984,0.00204903,0.0006623713,0.002205349,0.001559263,0.002232986,0.004182331],"category_scores_gemma":[0.03169244,0.001199073,0.001869445,0.001209719,0.0007882348,0.00327595,0.001413041,0.001760202,0.0006129249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001823147,"about_ca_system_score_gemma":0.001806982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00950902,"about_ca_topic_score_gemma":0.0076297,"domain_scores_codex":[0.9978218,0.00107375,0.0001761938,0.0003787215,0.0004305386,0.0001189174],"domain_scores_gemma":[0.9883454,0.008915367,0.0005624999,0.0007597425,0.001253543,0.0001636002],"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":[0.0002216826,0.00009678068,0.001694158,0.000158069,0.0002446751,0.00007639048,0.0001062488,0.8859272,0.001411045,0.04349676,0.001390064,0.06517695],"study_design_scores_gemma":[0.0000378649,0.00003098414,0.0006088474,0.00003204466,0.00006793184,0.00001314779,0.00001532322,0.9591153,0.0005034313,0.03909983,0.0004413373,0.00003392537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03743803,0.0003591941,0.9567778,0.00082092,0.0001103565,0.00008283671,0.0006190947,0.0004079626,0.003383793],"genre_scores_gemma":[0.7280785,0.0004452221,0.2674847,0.0002897089,0.0001266468,0.0003269743,0.001470989,0.0002172176,0.001559949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00950902,"threshold_uncertainty_score":0.04299521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04420250402777367,"score_gpt":0.2637485783549614,"score_spread":0.2195460743271877,"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."}}