{"id":"W2767104386","doi":"10.1111/ddi.12669","title":"Invasion lags: The stories we tell ourselves and our inability to infer process from pattern","year":2017,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biological dispersal; Lag; Post hoc; Narrative; Alien species; Population; Ecology; Process (computing); History; Geography; Invasive species; Biology; Computer science; Demography; Sociology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00009412189,0.0000548879,0.00006068969,0.000004878393,0.004754214,0.00004202706,0.0001383698,0.00003557202,0.0000308725],"category_scores_gemma":[0.0001526096,0.00004115827,0.00001270207,0.00002408732,0.0002756222,0.0002006652,0.001271464,0.00006559802,0.00002322141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000299674,"about_ca_system_score_gemma":0.000003674626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001317577,"about_ca_topic_score_gemma":0.006608675,"domain_scores_codex":[0.9996366,0.00002256385,0.00004313824,0.0001459052,0.00006321738,0.0000885988],"domain_scores_gemma":[0.9997202,0.00004162403,0.00003579603,0.0001383513,0.00001399072,0.00005007218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004724153,0.00002714748,0.9943165,0.000003087731,0.000007401674,6.644868e-7,0.004393857,0.00001031561,0.000005828801,0.0001805046,0.0004626113,0.0005873246],"study_design_scores_gemma":[0.0001046365,0.00001675159,0.9912909,0.000004022053,0.00002074034,3.108991e-7,0.004128095,0.00006032272,0.00002222293,0.003704427,0.0005917792,0.00005576058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990055,0.00002482455,0.0008340259,0.008380779,0.00006233364,0.00009583936,0.0002407022,0.0000099521,0.0002965388],"genre_scores_gemma":[0.9996987,0.00005598636,0.00001639643,0.0001154696,0.00001054782,0.000004422725,0.00001575995,7.835216e-7,0.00008196245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009643666,"threshold_uncertainty_score":0.9965414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256904366068302,"score_gpt":0.2650132955650161,"score_spread":0.2393228589581859,"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."}}