{"id":"W1990890161","doi":"10.1016/j.forpol.2007.12.002","title":"A marginal cost analysis of trade-offs in old-growth preservation in Ontario","year":2008,"lang":"en","type":"article","venue":"Forest Policy and Economics","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Time horizon; Maximization; Marginal cost; Opportunity cost; Profit (economics); Economics; Volume (thermodynamics); Forest management; Natural resource economics; Environmental resource management; Business; Environmental science; Agroforestry; Microeconomics; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001267065,0.00009225073,0.0001995383,0.0003714954,0.00003401619,0.00001052821,0.0001277796,0.00005119132,0.0003913454],"category_scores_gemma":[0.00002037845,0.00009940658,0.00004810204,0.0004512275,0.0001262468,0.0003388128,0.00008901978,0.00007921984,0.00002757305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003055111,"about_ca_system_score_gemma":0.00002743435,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5203083,"about_ca_topic_score_gemma":0.865337,"domain_scores_codex":[0.9992951,0.00001447823,0.0002597076,0.0001746966,0.00004234226,0.0002136909],"domain_scores_gemma":[0.999709,0.00002791662,0.00007740105,0.0001332666,0.00000131954,0.00005116237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002789816,0.00004426934,0.9619476,0.000004244431,0.0000227687,0.000001936175,0.00149094,0.02410423,0.000001892637,0.0118644,0.0003631059,0.0001267744],"study_design_scores_gemma":[0.0003759282,0.00003000722,0.970176,0.000003087041,0.0000202732,0.000001183211,0.000007814881,0.02193846,0.000004345762,0.00190732,0.005442512,0.00009303197],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97649,0.000003529808,0.000003257247,0.0005513089,0.00001257158,0.0001436552,0.000008773457,0.000004047527,0.02278282],"genre_scores_gemma":[0.9971511,0.0001064765,0.00008619542,0.0003296657,0.00001977713,0.00001352035,0.0000303116,0.000005437802,0.00225755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3450287,"threshold_uncertainty_score":0.482886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02092552091401555,"score_gpt":0.2169628699304314,"score_spread":0.1960373490164159,"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."}}