{"id":"W2905454201","doi":"10.1007/s10530-018-1890-1","title":"Turning population viability analysis on its head: using stochastic models to evaluate invasive species control strategies","year":2018,"lang":"en","type":"article","venue":"Biological Invasions","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada; Ministry of Environment","funders":"","keywords":"Biology; Invasive species; Population; Cost–benefit analysis; Population viability analysis; Population size; Ranking (information retrieval); Control (management); Ecology; Computer science; Machine learning; Endangered species; Artificial intelligence; Demography; Habitat","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005109016,0.0001896292,0.000323057,0.00008477747,0.0005045684,0.00003791625,0.0002124262,0.000186871,0.008296266],"category_scores_gemma":[0.000477801,0.0001300831,0.000122678,0.0005652356,0.0003283869,0.0002100098,0.0001486119,0.0001517057,0.001264881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288485,"about_ca_system_score_gemma":0.00002649119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003084963,"about_ca_topic_score_gemma":0.001655137,"domain_scores_codex":[0.9982685,0.0003319253,0.0003149591,0.0005074622,0.0002203156,0.0003568674],"domain_scores_gemma":[0.9990505,0.0003943292,0.0001043831,0.0002545193,0.0000398757,0.0001564104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001556107,0.0003419434,0.1281647,0.000002364017,0.0001085048,0.000006486412,0.0005984406,0.7549098,0.1127482,0.002518762,0.0001571194,0.0002881982],"study_design_scores_gemma":[0.0003094646,0.0008050672,0.7804284,0.00001385201,0.00028672,0.00000207795,0.0004015305,0.207647,0.0009121376,0.008887911,0.000007650281,0.0002981749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590523,0.000003010588,0.03942545,0.0001619778,0.0001130283,0.000475687,0.0000252412,0.00004950535,0.0006937915],"genre_scores_gemma":[0.9980825,9.315755e-7,0.0009798019,0.0007232439,0.00006846117,0.0000351026,0.00002560317,0.000005716305,0.0000786613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6522637,"threshold_uncertainty_score":0.9995127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3306536329177874,"score_gpt":0.3394873017810497,"score_spread":0.008833668863262256,"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."}}