{"id":"W2519745015","doi":"10.3897/neobiota.31.10038","title":"Confronting the wicked problem of managing biological invasions","year":2016,"lang":"en","type":"article","venue":"NeoBiota","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; The Scarborough Hospital; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; DST-NRF Centre of Excellence for Invasion Biology; Division of Materials Research; Department of Science and Technology, Ministry of Science and Technology, India; National Research Foundation","keywords":"Wickedness; Wicked problem; Anthropocene; Environmental resource management; Scope (computer science); Risk analysis (engineering); Computer science; Ecology; Biology; Business; Epistemology; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001370215,0.00005326497,0.00006361541,0.000005735207,0.00008685766,0.000006785989,0.0001847311,0.00003175551,0.03227047],"category_scores_gemma":[0.00002497238,0.00002394303,0.00003491535,0.00008629817,0.0003894278,0.00003822638,0.0001802502,0.00003302663,0.0006787316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007003365,"about_ca_system_score_gemma":0.000002203111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007000839,"about_ca_topic_score_gemma":0.00006240099,"domain_scores_codex":[0.999499,0.00002750967,0.0001081694,0.000122771,0.0000921786,0.0001503335],"domain_scores_gemma":[0.9997104,0.00005602988,0.0000524366,0.0001462484,0.000004368595,0.00003052137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002136777,0.00009772807,0.144026,0.000006882809,0.00001428544,0.000003427956,0.0005285168,0.000001578961,0.7458156,0.02429328,0.03916547,0.04602591],"study_design_scores_gemma":[0.0006795796,0.0001040964,0.5020072,0.00005168118,0.00001290705,0.00001264967,0.003623159,0.00001871365,0.0203609,0.001904839,0.470988,0.0002363488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023259,0.00003612199,0.0001663104,0.004769814,0.00005030707,0.0001265506,0.00002118762,0.00002912694,0.09247469],"genre_scores_gemma":[0.9986625,0.00006160256,0.00009008761,0.0001938051,0.00001131399,0.000006251583,0.00000315528,0.000002904355,0.0009684042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7254547,"threshold_uncertainty_score":0.9686142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07017811404536205,"score_gpt":0.2375942424181789,"score_spread":0.1674161283728168,"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."}}