{"id":"W2080982626","doi":"10.1139/x08-055","title":"Hierarchical forest management with anticipation: an application to tactical–operational planning integration","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Anticipation (artificial intelligence); Operational planning; Operations research; Process (computing); Plan (archaeology); Computer science; Key (lock); Task (project management); Strategic planning; Capacity planning; Decomposition; Process management; Operations management; Systems engineering; Business; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007963868,0.000107587,0.0001145234,0.0005145548,0.0005618663,0.0001100963,0.0004115089,0.00004844306,0.000690812],"category_scores_gemma":[0.00007488579,0.00008653913,0.00003040912,0.0007380439,0.000348567,0.0005267512,0.00004435461,0.0003516793,0.0003980055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003693656,"about_ca_system_score_gemma":0.0002066457,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00582578,"about_ca_topic_score_gemma":0.06619474,"domain_scores_codex":[0.9981829,0.0001055818,0.0002664751,0.0002041613,0.0007716769,0.0004692055],"domain_scores_gemma":[0.9986806,0.00004857352,0.00005994673,0.0002296916,0.0001079179,0.0008732875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001226502,0.00005768107,0.8979204,0.00000786317,0.00002181584,0.0003611605,0.001428586,0.05685358,0.00004613185,0.01846601,0.01877091,0.005943182],"study_design_scores_gemma":[0.0003412332,0.0007166925,0.9431043,0.00003868276,0.000008964773,0.0001295236,0.0001077475,0.00514044,0.00002685895,0.001032013,0.04921557,0.0001380077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673702,0.00001007525,0.009171421,0.002212067,0.00004483574,0.0003832628,0.000002835925,0.000006344704,0.02079896],"genre_scores_gemma":[0.9941804,0.000005379145,0.003904454,0.0002501397,0.0001992286,0.00003113865,0.00002217541,0.00001550033,0.001391518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06036896,"threshold_uncertainty_score":0.9508448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05352349198165961,"score_gpt":0.3397407611375056,"score_spread":0.2862172691558459,"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."}}