{"id":"W2018247661","doi":"10.1016/j.foreco.2006.11.007","title":"Forest planning using co-evolutionary cellular automata","year":2006,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources","keywords":"Time horizon; Computer science; Cellular automaton; Forest management; Interdependence; Spatial planning; Maximization; Operations research; Environmental resource management; Mathematical optimization; Geography; Environmental science; Environmental planning; Artificial intelligence; Mathematics; Forestry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005920993,0.000598009,0.001135768,0.0007703791,0.0009686604,0.001495136,0.001139139,0.001427533,0.002872797],"category_scores_gemma":[0.003928828,0.0008253736,0.001105938,0.000994096,0.001151069,0.001366435,0.001348378,0.001038341,0.0002159715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001493288,"about_ca_system_score_gemma":0.001029727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02360857,"about_ca_topic_score_gemma":0.0177132,"domain_scores_codex":[0.9996911,0.0001097201,0.00002103667,0.00007448396,0.00005276539,0.00005084769],"domain_scores_gemma":[0.9974119,0.001954746,0.0001398921,0.0001389078,0.0002296206,0.0001249493],"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":[0.00000893513,0.000005661884,0.0002827396,0.000006747196,0.00001313552,0.00002371568,0.00001734643,0.9916731,0.0001171607,0.005018417,0.00009373295,0.002739293],"study_design_scores_gemma":[0.000002019282,0.00000243042,0.00002873426,0.000001116748,0.000002757722,0.000003915306,0.000002829213,0.9967855,0.00003789238,0.003076615,0.00005438377,0.000001900721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1398914,0.0004471406,0.8467395,0.0005520186,0.000168562,0.00006706703,0.0001684749,0.0004274294,0.01153834],"genre_scores_gemma":[0.9596746,0.0001634795,0.03733604,0.00005570476,0.0000240924,0.0001041323,0.00007238257,0.00004181255,0.002527636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02360857,"threshold_uncertainty_score":0.04694229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043491380470308,"score_gpt":0.232995028203049,"score_spread":0.2225601143983459,"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."}}