{"id":"W2117843969","doi":"10.1139/x06-108","title":"Using Bayesian belief networks in adaptive management","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":249,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adaptive management; Bayesian network; Computer science; Bayesian probability; Process (computing); Influence diagram; Management science; Operations research; Decision tree; Artificial intelligence; Machine learning; Risk analysis (engineering); Engineering; Environmental resource management; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01204604,0.001413002,0.001271518,0.004124298,0.001180604,0.005439236,0.002043667,0.003121471,0.00448297],"category_scores_gemma":[0.04022342,0.001248719,0.001292396,0.00450587,0.004516796,0.008942509,0.002497183,0.003451519,0.000613338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004409108,"about_ca_system_score_gemma":0.002557064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02140829,"about_ca_topic_score_gemma":0.01378641,"domain_scores_codex":[0.9926184,0.005385373,0.0003831149,0.0006477108,0.0008105803,0.0001548829],"domain_scores_gemma":[0.9715801,0.02496163,0.001197319,0.000801257,0.001156216,0.0003033581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000341982,0.0000344521,0.00138575,0.0002876853,0.0001375479,0.0001321367,0.0004587563,0.3580821,0.0001391162,0.5809606,0.001545595,0.05680209],"study_design_scores_gemma":[0.00001654769,0.00001103246,0.0001956021,0.0001443445,0.00003314397,0.00003339004,0.00009084355,0.2683839,0.0001190548,0.7237958,0.007139521,0.00003674579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003033642,0.002438658,0.9834801,0.002167565,0.0001158303,0.0000563685,0.000124153,0.0001618589,0.008421915],"genre_scores_gemma":[0.2988066,0.008155718,0.687719,0.000708092,0.0004969742,0.0005641922,0.0003092158,0.0001077457,0.003132409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02140829,"threshold_uncertainty_score":0.06370628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419425602060646,"score_gpt":0.3032468153849311,"score_spread":0.2590525593643246,"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."}}