{"id":"W2093428443","doi":"10.1016/j.foreco.2005.12.001","title":"An application of fuzzy set theory for seral-class constraints in forest planning models","year":2006,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"National Science Council","keywords":"Seral community; Mathematics; Ecology; Ecological succession","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003782355,0.0001169708,0.0001401662,0.000101045,0.00007906543,0.00001155114,0.0001656647,0.00006973856,0.00007483288],"category_scores_gemma":[0.000002668659,0.0001151514,0.00002719791,0.0001070101,0.0002768552,0.0001925478,0.00011946,0.00004519139,0.00002722448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004294611,"about_ca_system_score_gemma":0.000002444621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002267699,"about_ca_topic_score_gemma":0.002635743,"domain_scores_codex":[0.9991275,0.00003714358,0.0002228173,0.0002674323,0.00007534257,0.0002697586],"domain_scores_gemma":[0.9996392,0.00004634259,0.00009117553,0.0001842994,0.00000389512,0.00003510279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00006270556,0.00009534339,0.2921502,0.00005952991,0.00001360795,0.00000409128,0.0001315755,0.1574957,0.00001376222,0.5452736,0.002601475,0.002098404],"study_design_scores_gemma":[0.0008507618,0.0001390103,0.5895023,0.000008669235,0.00002906132,0.000001199595,0.00008587589,0.07191414,0.000006514722,0.3317181,0.005603659,0.00014074],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9165952,0.00001631801,0.01429233,0.0001687096,0.00005641394,0.001219168,0.00001837724,0.00003125161,0.06760228],"genre_scores_gemma":[0.9970393,0.00001291318,0.001312804,0.0002037771,0.0000236459,0.0002690849,0.0001078965,0.00001030682,0.001020282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2973521,"threshold_uncertainty_score":0.4695737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009959016662258336,"score_gpt":0.2467552113533545,"score_spread":0.2367961946910961,"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."}}