{"id":"W2061663415","doi":"10.1016/j.jmarsys.2007.07.004","title":"Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management","year":2007,"lang":"en","type":"article","venue":"Journal of Marine Systems","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian inference; Joint probability distribution; Probability distribution; Uncertainty quantification; Bayes' theorem; Environmental science; Statistical inference; Computer science; Markov chain Monte Carlo; Inference; Posterior probability; Bayesian probability; Statistics; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007979894,0.0007159805,0.0009765257,0.0008354604,0.0005613471,0.001358788,0.001621373,0.002064698,0.001926865],"category_scores_gemma":[0.06086599,0.0009455839,0.0007338239,0.0006429103,0.0009855466,0.003314423,0.001471047,0.001867218,0.0002040272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140976,"about_ca_system_score_gemma":0.001193719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006492085,"about_ca_topic_score_gemma":0.005201005,"domain_scores_codex":[0.9986009,0.0009795714,0.00005191092,0.0001541627,0.0001457513,0.00006772262],"domain_scores_gemma":[0.9729241,0.02247434,0.001425297,0.001601315,0.001290774,0.000284145],"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":[0.00009790256,0.00004590198,0.002671538,0.00002149691,0.00004157665,0.00001632382,0.00003034957,0.974889,0.0002523189,0.01203928,0.0002856924,0.009608645],"study_design_scores_gemma":[0.00002150399,0.00001414139,0.0006921457,0.000007907646,0.00001209509,0.000007051696,0.000007754012,0.9773218,0.0001033325,0.02171296,0.00008823582,0.00001113467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5089136,0.0006186981,0.4817246,0.002565517,0.00009017865,0.00006177164,0.0004446998,0.000547639,0.005033221],"genre_scores_gemma":[0.9748539,0.0001551613,0.02389367,0.0001185639,0.00003552817,0.00003816508,0.0002348406,0.00007448177,0.0005955744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007979894,"threshold_uncertainty_score":0.04220223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120684074823903,"score_gpt":0.1884690615867863,"score_spread":0.176400654104396,"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."}}