{"id":"W2045906895","doi":"10.1016/j.na.2009.04.025","title":"Optimal harvesting of diffusive models in a nonhomogeneous environment","year":2009,"lang":"en","type":"article","venue":"Nonlinear Analysis","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Mary's University; Athabasca University; University of Calgary","funders":"","keywords":"Gompertz function; Diffusion; Limit (mathematics); Type (biology); Variety (cybernetics); Yield (engineering); Production (economics); Mathematical optimization; Function (biology); Mathematics; Logistic function; Reaction–diffusion system; Computer science; Applied mathematics; Mathematical analysis; Economics; Statistics; Physics; Thermodynamics; Ecology; Microeconomics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003702154,0.0001063604,0.0006802606,0.0001790749,0.00002368383,0.000001595043,0.00006144433,0.0001340047,0.0005840795],"category_scores_gemma":[0.0001612897,0.00008002161,0.0002630262,0.0002742849,0.000124848,0.00002643381,0.000022172,0.0001601741,0.00001697563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001950424,"about_ca_system_score_gemma":0.00001575859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000191094,"about_ca_topic_score_gemma":0.000006912145,"domain_scores_codex":[0.9989693,0.00006004589,0.0004378876,0.0002155422,0.0001050683,0.0002121584],"domain_scores_gemma":[0.999392,0.0001953626,0.00008004243,0.0002077735,0.00002242517,0.0001023654],"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":[0.001216507,0.006278868,0.04267749,0.0002052553,0.003422064,0.0005079024,0.001412686,0.7812321,0.004244041,0.1446628,0.00002835832,0.014112],"study_design_scores_gemma":[0.0004583866,0.0002305638,0.00596201,0.00001744495,0.0008465269,0.000011028,0.00002729871,0.9786471,0.0003998977,0.01330693,0.00001363553,0.00007922591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630387,0.00008137755,0.1311228,0.0006983856,0.000004825672,0.000100534,0.000007105141,0.00001157318,0.004934745],"genre_scores_gemma":[0.9705622,0.00003605567,0.02842046,0.0004462333,0.00002507892,0.000003989771,0.00002142471,0.000003939675,0.0004806571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.197415,"threshold_uncertainty_score":0.6395261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0228027458701014,"score_gpt":0.2803196142877118,"score_spread":0.2575168684176105,"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."}}