{"id":"W2963213703","doi":"10.7717/peerj.7350","title":"Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie","year":2019,"lang":"en","type":"article","venue":"PeerJ","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ocean University of China","keywords":"Geographically Weighted Regression; Perch; Spatial distribution; Generalized additive model; Regression; Spatial ecology; Spatial variability; Habitat; Spatial heterogeneity; Regression analysis; Environmental science; Species distribution; Distribution (mathematics); Geography; Ecology; Physical geography; Statistics; Fish <Actinopterygii>; Fishery; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"category_scores_codex":[0.0001056914,0.0001150569,0.0001037233,0.0000194823,0.00005887066,0.00001691989,0.00009927476,0.00003988217,0.05821037],"category_scores_gemma":[0.00001921796,0.0001162358,0.00004486659,0.0001111032,0.00005516444,0.0002100687,0.00008321623,0.000114326,0.01006765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006298628,"about_ca_system_score_gemma":0.000003144468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006061345,"about_ca_topic_score_gemma":0.001148189,"domain_scores_codex":[0.9990191,0.00003739853,0.000121835,0.0002623665,0.0003186426,0.0002405963],"domain_scores_gemma":[0.9996869,0.00005984299,0.00003069596,0.0001558159,0.000001385901,0.00006534371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002409988,0.0006157291,0.8922857,0.00004196436,0.00001116188,0.00005745181,0.001154146,0.0005575927,0.03879014,0.0005813349,0.006223851,0.05943993],"study_design_scores_gemma":[0.0005679713,0.0001005767,0.9198253,0.00001198716,0.000003072325,0.000002467016,0.0005146089,0.0002650434,0.003273023,0.00002079924,0.07526743,0.0001477487],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872967,0.000007529282,0.00005241486,0.0002856002,0.0002902045,0.0002394948,0.000533446,0.00003056662,0.01126403],"genre_scores_gemma":[0.9977977,0.00004784004,0.000009853291,0.0001365271,0.00003315285,0.00006182455,0.001400944,0.000009303755,0.0005028785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06904358,"threshold_uncertainty_score":0.9907031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03198908877230641,"score_gpt":0.2207014326034154,"score_spread":0.188712343831109,"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."}}