{"id":"W3201053833","doi":"10.1093/forsci/fxab025","title":"Inter-Temporal Aggregation for Spatially Explicit Optimal Harvest Scheduling under Area Restrictions","year":2021,"lang":"en","type":"article","venue":"Forest Science","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of British Columbia","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Time horizon; Scheduling (production processes); Computer science; Set (abstract data type); Mathematical optimization; Duration (music); Temporal database; Mathematics; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002410818,0.0006624632,0.001765154,0.0007340778,0.0008918568,0.001425551,0.001820549,0.001105237,0.003460508],"category_scores_gemma":[0.006226156,0.0008764267,0.0007044466,0.00149673,0.0008278177,0.001980345,0.001866876,0.001150497,0.0002535414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143092,"about_ca_system_score_gemma":0.002466197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01720915,"about_ca_topic_score_gemma":0.02112106,"domain_scores_codex":[0.9992219,0.0002217536,0.00004801245,0.0001339606,0.0001146761,0.000259591],"domain_scores_gemma":[0.9963785,0.00262889,0.0002954208,0.0002289738,0.000233149,0.0002351179],"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":[0.00009843363,0.00004597618,0.000483896,0.00002505835,0.00002111941,0.00003692205,0.00004757974,0.9809741,0.0004662831,0.007057928,0.0005042059,0.01023848],"study_design_scores_gemma":[0.000004696377,0.000006609765,0.00009005493,0.000001841265,0.000003527616,0.000003429421,0.00001012661,0.9971046,0.00004963943,0.002661326,0.00006238322,0.000001880403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1485014,0.0005608033,0.845082,0.0004821506,0.0001529268,0.0001098036,0.0003034286,0.0003626455,0.004444912],"genre_scores_gemma":[0.9014086,0.0002126395,0.09504849,0.00008996083,0.00009000426,0.0001561179,0.0002089602,0.00008959182,0.002695662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01720915,"threshold_uncertainty_score":0.03421795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837882764173367,"score_gpt":0.2694355347745452,"score_spread":0.2410567071328116,"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."}}