{"id":"W4311500695","doi":"10.1016/j.jenvman.2022.116855","title":"A bi-level model for state and county aquatic invasive species prevention decisions","year":2022,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Recreation; Planner; Plan (archaeology); Watercraft; Resource (disambiguation); State (computer science); Operations research; Environmental science; Environmental resource management; Geography; Computer science; Engineering; Ecology; Marine engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002604705,0.001351276,0.002228815,0.001147974,0.001229265,0.004018367,0.00453161,0.004210477,0.02385494],"category_scores_gemma":[0.004683149,0.001547644,0.001709722,0.002127218,0.001761955,0.003328022,0.002347867,0.003908922,0.00233071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005318533,"about_ca_system_score_gemma":0.005693488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06135838,"about_ca_topic_score_gemma":0.04538327,"domain_scores_codex":[0.9982034,0.000590283,0.00007069308,0.0004295245,0.0001997659,0.0005063419],"domain_scores_gemma":[0.9968274,0.001778384,0.000417866,0.0001085587,0.0004733809,0.0003943686],"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":[0.00008349455,0.00006935184,0.001023299,0.00003852196,0.00003811604,0.0001022161,0.00006515344,0.9706206,0.0002179672,0.02384952,0.001422656,0.002469048],"study_design_scores_gemma":[0.00005136089,0.00003345459,0.0003594353,0.000009152066,0.00002084774,0.00001715238,0.00004344094,0.9911504,0.00003799375,0.007160438,0.00110023,0.00001611646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1728459,0.0009876798,0.741273,0.006443802,0.0002996335,0.0006643014,0.007160407,0.001137726,0.06918744],"genre_scores_gemma":[0.8551991,0.0008830905,0.08400446,0.0006836129,0.0001208801,0.001236094,0.002375515,0.0001874225,0.05530981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06135838,"threshold_uncertainty_score":0.1220025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04084000102150451,"score_gpt":0.2421501627290205,"score_spread":0.201310161707516,"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."}}