{"id":"W2130905133","doi":"10.1890/06-0239","title":"PREDICTING INVASION RISK USING MEASURES OF INTRODUCTION EFFORT AND ENVIRONMENTAL NICHE MODELS","year":2007,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Marine Ecology and Invasive Species","field":"Environmental Science","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Windsor","funders":"University of Alberta","keywords":"Biological dispersal; Eriocheir; Range (aeronautics); Habitat; Fishery; Ecology; Geography; Chinese mitten crab; Environmental niche modelling; Niche; Ecological niche; Environmental science; Biology; Population","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":[0.0005217739,0.0000845197,0.0001123369,0.00002578744,0.000273988,0.000005184176,0.0001017089,0.000101539,0.001110899],"category_scores_gemma":[0.0000523851,0.00007164237,0.00003087754,0.0001079422,0.0003890484,0.0001259189,0.0002164583,0.0001454653,0.0000239982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009825308,"about_ca_system_score_gemma":0.000003256437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005938615,"about_ca_topic_score_gemma":0.0001900788,"domain_scores_codex":[0.9991684,0.00002929267,0.0002380271,0.0002735856,0.0001229198,0.0001677922],"domain_scores_gemma":[0.9995537,0.0000883668,0.0001375253,0.0001529469,0.000004364495,0.00006304734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001291417,0.0001806998,0.9719791,0.000002708687,0.000008844659,3.6437e-7,0.00005921242,0.003416402,0.01839864,0.001191854,0.0001815978,0.004567685],"study_design_scores_gemma":[0.0001364233,0.00008324742,0.9782566,7.401642e-7,0.00002755555,0.00001082698,0.0001603286,0.006172491,0.004726761,0.006913059,0.003419385,0.0000926241],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976126,0.00002738801,0.01950117,0.00007920946,0.00002695812,0.0003495739,0.000007749738,0.00002444436,0.003857484],"genre_scores_gemma":[0.9944563,0.00008849275,0.005157156,0.00004448689,0.00009961543,0.00003654043,0.00001474395,0.00000458061,0.00009808798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01833027,"threshold_uncertainty_score":0.9998022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475145242360457,"score_gpt":0.224002966358894,"score_spread":0.1992515139352894,"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."}}