{"id":"W4401503817","doi":"10.1139/cjfr-2023-0227","title":"Integrated planning for a multiproduct, multisite reforestation value chain: a Canadian case study","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Centre de Géomatique du Québec; Université Laval","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Reforestation; Product (mathematics); Value (mathematics); Chain (unit); Forestry; Environmental science; Agroforestry; Geography; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072723,0.000907241,0.0004221796,0.001361012,0.003922656,0.002425857,0.001713339,0.001549047,0.005185028],"category_scores_gemma":[0.001666394,0.0005583448,0.0008448714,0.003173264,0.001532961,0.001181127,0.001092567,0.001102386,0.000265268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03294246,"about_ca_system_score_gemma":0.03146056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9171122,"about_ca_topic_score_gemma":0.9637158,"domain_scores_codex":[0.9989058,0.0002091155,0.0000266456,0.0001242549,0.0002759806,0.0004581777],"domain_scores_gemma":[0.9988826,0.0003732001,0.0000857035,0.0000701902,0.0003123182,0.0002759542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002628855,0.000575537,0.01983956,0.0002098396,0.00007146996,0.004620639,0.001278525,0.8750173,0.004248478,0.03588523,0.003823903,0.05416666],"study_design_scores_gemma":[0.0002438068,0.0006303453,0.02060219,0.0001503605,0.0001223367,0.0006842255,0.008099299,0.9198232,0.003787439,0.009752264,0.035926,0.0001786107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8629946,0.0006768364,0.05018023,0.001304997,0.00003427442,0.001511228,0.001618516,0.0002471746,0.08143231],"genre_scores_gemma":[0.9422584,0.0004550377,0.04280783,0.00007354428,0.00000757389,0.00017177,0.0004959557,0.00004225307,0.01368767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08288777,"threshold_uncertainty_score":0.2390154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07309087810543013,"score_gpt":0.3687297648355457,"score_spread":0.2956388867301156,"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."}}