{"id":"W2106642978","doi":"10.1139/x03-217","title":"Incorporating average and maximum area restrictions in harvest scheduling models","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Forest Service","keywords":"Scheduling (production processes); Computer science; Recreation; Forest management; Environmental science; Environmental resource management; Operations research; Mathematical optimization; Ecology; Mathematics; Agroforestry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001167987,0.00008590255,0.0001160526,0.0006811098,0.0003065545,0.0001596697,0.000296021,0.00005880295,0.0002007689],"category_scores_gemma":[0.00020117,0.00008143609,0.00003181813,0.0008171712,0.0003548919,0.0006662068,0.00007386188,0.0005623497,0.00007734179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005971202,"about_ca_system_score_gemma":0.0004216897,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1245967,"about_ca_topic_score_gemma":0.6060964,"domain_scores_codex":[0.9986749,0.00006681921,0.0002738322,0.0001411245,0.0003515792,0.000491699],"domain_scores_gemma":[0.9991019,0.00005376338,0.00007467665,0.0001405779,0.00003589579,0.0005931463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009290206,0.000016387,0.5955647,0.000008027788,0.000005727808,0.0004536828,0.0005688502,0.3902977,0.00003426784,0.01109192,0.0008366944,0.001112752],"study_design_scores_gemma":[0.001566648,0.0003794866,0.702351,0.0002748923,0.000007970536,0.0001961624,0.0003503863,0.01258315,0.00003072028,0.2763604,0.005598932,0.0003002018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754393,0.0001029866,0.0003063853,0.00155932,0.0000544245,0.0001268773,0.000002790597,0.000002788076,0.02240513],"genre_scores_gemma":[0.9978803,0.00004308342,0.001447928,0.00005226137,0.0000654082,0.000003347088,0.000001371503,0.00001135239,0.0004949431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4814997,"threshold_uncertainty_score":0.8812327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05880343771744057,"score_gpt":0.2912329284315832,"score_spread":0.2324294907141426,"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."}}