{"id":"W1553125145","doi":"10.1111/j.1472-4642.2011.00791.x","title":"A mechanistic model for understanding invasions: using the environment as a predictor of population success","year":2011,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Marine Ecology and Invasive Species","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; University of Alberta; Dalhousie University","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Alberta","keywords":"Salinity; Estuary; Population; Copepod; Ecology; Population growth; Habitat; Population model; Niche; Range (aeronautics); Subspecies; Temperature salinity diagrams; Environmental change; Environmental science; Biology; Geography; Climate change; Demography; Crustacean","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001007579,0.0008076348,0.0006011334,0.0007148536,0.0005753135,0.001212858,0.002252247,0.001597033,0.003749809],"category_scores_gemma":[0.002625124,0.0006013655,0.001177175,0.0004621657,0.000899269,0.002402949,0.001098897,0.001245721,0.0004360676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083797,"about_ca_system_score_gemma":0.001325151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01132367,"about_ca_topic_score_gemma":0.007951039,"domain_scores_codex":[0.9997614,0.00007886934,0.00001508099,0.00007335989,0.00003508923,0.00003616436],"domain_scores_gemma":[0.999326,0.0003170491,0.000164042,0.00004989664,0.00008245291,0.0000606464],"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":[0.00001418032,0.00004680382,0.007089246,0.00005581042,0.00007385716,0.0001156253,0.0001072591,0.9613822,0.001702373,0.02510326,0.0004698216,0.00383967],"study_design_scores_gemma":[0.00001034504,0.00002902905,0.002416584,0.00001464294,0.00002743193,0.00008333258,0.00004824943,0.9793448,0.0001263742,0.01657429,0.001305424,0.00001951548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3052367,0.0005936399,0.6699539,0.002715789,0.0001366024,0.0001557454,0.001602588,0.0002987436,0.01930628],"genre_scores_gemma":[0.9539142,0.0007771536,0.03399534,0.00040538,0.00009004326,0.0003906798,0.0004939238,0.00008867508,0.009844682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01132367,"threshold_uncertainty_score":0.02251548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1481491724541951,"score_gpt":0.2372613359468534,"score_spread":0.0891121634926583,"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."}}