{"id":"W2143512613","doi":"10.1111/ddi.12358","title":"Optimal allocation of invasive species surveillance with the maximum expected coverage concept","year":2015,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Natural Resources Canada; Ontario Forest Research Institute; Canadian Forest Service","funders":"","keywords":"Emerald ash borer; Propagule pressure; PEST analysis; Destinations; Agrilus; Geography; Fraxinus; Ecology; Invasive species; Transmission (telecommunications); Biological dispersal; Biology; Computer science; Demography; Population; Tourism","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007331217,0.0000432027,0.00005190964,0.000006043321,0.0004304062,0.00000685792,0.00009238176,0.0000204601,0.0001979347],"category_scores_gemma":[0.00002913473,0.00003114263,0.00001170907,0.0001060575,0.0004586919,0.00009787576,0.0004297099,0.00003507628,0.00001549453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000505859,"about_ca_system_score_gemma":0.000007951189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003835366,"about_ca_topic_score_gemma":0.0007468407,"domain_scores_codex":[0.999669,0.00002912781,0.00003932746,0.00008782147,0.00009541837,0.00007926995],"domain_scores_gemma":[0.9997874,0.00003605465,0.00003758067,0.00008266675,0.00001844857,0.00003786865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001361491,0.0002063763,0.9187152,0.000006115137,0.00005947015,0.0000119358,0.005978819,0.009873698,0.000148442,0.02260063,0.04215325,0.0001099332],"study_design_scores_gemma":[0.0003827255,0.0001137914,0.9928628,0.000001506382,0.00001478674,0.000002425804,0.002465172,0.00004295927,0.0001770417,0.0003196189,0.003558967,0.00005815941],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860356,0.00001412523,0.009101148,0.0004990764,0.00002641923,0.0001078851,0.0001079488,0.0000108489,0.004096948],"genre_scores_gemma":[0.9995856,0.00001166696,0.00005333514,0.00005169344,0.00000370874,0.000002557181,0.00008207169,7.631116e-7,0.0002086558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07414767,"threshold_uncertainty_score":0.331038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157630505603801,"score_gpt":0.1903881158855746,"score_spread":0.1746250653251945,"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."}}