{"id":"W2034929555","doi":"10.3808/jei.200900137","title":"Accounting for the Influence of Geographic Location and Spatial Autocorrelation in Environmental Models: A Comparative Analysis Using North American Songbirds","year":2009,"lang":"en","type":"article","venue":"Journal of Environmental Informatics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. Geological Survey; University of Calgary","keywords":"Spatial analysis; Autocorrelation; Residual; Contrast (vision); Breeding bird survey; Goodness of fit; Statistics; Geography; Logistic regression; Econometrics; Generalized additive model; Mathematics; Ecology; Computer science; Habitat; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0004833477,0.0001159246,0.0003789835,0.0003143005,0.00007846179,0.0000253578,0.0001161469,0.00003554645,0.0000113101],"category_scores_gemma":[0.000008914031,0.0001092671,0.0001101459,0.0001719789,0.0001564246,0.0008945228,0.00002708886,0.0001219331,0.000002267886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002130254,"about_ca_system_score_gemma":0.000006571446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009767681,"about_ca_topic_score_gemma":0.00003492666,"domain_scores_codex":[0.9985408,0.00001195079,0.001163001,0.00008120082,0.00007445109,0.0001285779],"domain_scores_gemma":[0.9979793,0.00006543395,0.001804709,0.0001105816,0.000004227241,0.00003574635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001647424,0.00004740983,0.5258231,0.000004300452,0.00006749234,4.387566e-8,0.00141162,0.4715692,0.00003214941,0.00007565462,4.24882e-7,0.0009522323],"study_design_scores_gemma":[0.0002796922,0.00008287408,0.5578622,0.000005245014,0.00005890366,0.000002104785,0.0009471236,0.4404213,0.000008041722,0.0002647259,0.000007307058,0.00006043608],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527839,0.0001935875,0.04664984,0.00002588737,0.00002120061,0.0002451957,0.00005670054,0.000001403735,0.00002222963],"genre_scores_gemma":[0.9969718,0.0004770473,0.002425185,0.00008099792,0.00001771981,0.000003926767,0.00001707005,0.000004516038,0.000001727591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04422465,"threshold_uncertainty_score":0.4455785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04913453709627097,"score_gpt":0.2296323364837338,"score_spread":0.1804977993874629,"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."}}