{"id":"W2152306358","doi":"10.1287/inte.30.6.32.11625","title":"The Québec Ministry of Natural Resources Uses Linear Programming to Understand the Wood-Fiber Market","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts (Québec); Université Laval","funders":"","keywords":"Christian ministry; Government (linguistics); Negotiation; Linear programming; Natural resource; Industrial organization; Fiber; Yield (engineering); Business; Computer science; Environmental economics; Economics; Political science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004716905,0.0001425809,0.0001239187,0.00004106508,0.0004602736,0.0001853486,0.0005329513,0.0000397429,0.002906339],"category_scores_gemma":[0.00002703875,0.00006798137,0.0000953887,0.0003240228,0.0003025226,0.00009153374,0.00009348631,0.0003086457,0.0006709604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012305,"about_ca_system_score_gemma":0.00002506649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002205214,"about_ca_topic_score_gemma":0.0005373083,"domain_scores_codex":[0.9987255,0.0000131228,0.000352629,0.00009685,0.000498946,0.0003128966],"domain_scores_gemma":[0.9993201,0.0001389632,0.0001655675,0.0002574118,0.000008579414,0.0001093466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001327979,0.0001686068,0.005170372,0.00003115663,0.0003673143,0.00002162896,0.008225366,0.03596615,0.00003924383,0.002960397,0.2629917,0.6827301],"study_design_scores_gemma":[0.0002257952,0.00008909372,0.002658703,0.00001531112,0.00003194034,0.00001187585,0.0008557133,0.001298707,0.00003079897,0.0002095147,0.9944461,0.000126471],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7258034,0.0000337172,0.00001135117,0.003233036,0.00005313322,0.0002364426,0.000002170474,0.00001646386,0.2706102],"genre_scores_gemma":[0.9224803,0.00008205857,0.000299052,0.001288858,0.0001561031,0.000003235484,7.947219e-7,0.00001413332,0.07567541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7314544,"threshold_uncertainty_score":0.9980052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032985194353745,"score_gpt":0.2361617390863519,"score_spread":0.2258318871428144,"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."}}