{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009369603,0.00004751041,0.00005713866,0.0000107603,0.001286045,0.000003961153,0.00008490567,0.00003524436,0.0005700864],"category_scores_gemma":[0.00004942788,0.00003857265,0.00003036977,0.00003972926,0.0001999646,0.0001155599,0.0005350257,0.00003299832,0.000002670972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271783,"about_ca_system_score_gemma":0.000003948756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008341135,"about_ca_topic_score_gemma":0.0001672655,"domain_scores_codex":[0.999647,0.00001611334,0.00007090229,0.0001041015,0.00007081972,0.00009111039],"domain_scores_gemma":[0.9997923,0.00004443395,0.0000496765,0.00007656528,0.00000375062,0.0000333138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003856848,0.0001051438,0.6782879,0.00001080518,0.00002200011,6.145845e-7,0.001602927,0.001206476,0.0002727374,0.31829,0.0001419527,0.00002091813],"study_design_scores_gemma":[0.000440783,0.0001020272,0.4858153,0.000009154673,0.0001789303,0.000003118201,0.001569544,0.1216026,0.0002923901,0.3898162,0.00003411921,0.0001358467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6907251,0.000003729739,0.3082617,0.00003921346,0.00002149919,0.000161577,0.0001638769,0.000005367162,0.0006178737],"genre_scores_gemma":[0.9991474,0.00001046706,0.000711425,0.0000228304,0.000003095646,0.000004488479,0.00004102168,0.000001270893,0.00005802246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3084222,"threshold_uncertainty_score":0.989135,"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."}}