{"id":"W2145815707","doi":"10.1007/s00442-010-1867-y","title":"Modelling the effect of directional spatial ecological processes at different scales","year":2010,"lang":"en","type":"article","venue":"Oecologia","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":142,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada; Université du Québec à Montréal; Université de Montréal; University of Alberta","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Spatial distribution; Univariate; Spatial ecology; Sampling (signal processing); Spatial variability; Ecology; Multivariate statistics; Eigenfunction; Biology; Physical geography; Eigenvalues and eigenvectors; Geology; Statistics; Mathematics; Remote sensing; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001324908,0.00009199153,0.0001137941,0.000006943623,0.0001892545,0.0000117211,0.0001703427,0.00008378005,0.06879357],"category_scores_gemma":[0.0001182171,0.00004759293,0.00004862281,0.0000732043,0.000321841,0.00003005895,0.0001833705,0.0001256668,0.000403654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001293863,"about_ca_system_score_gemma":0.000002403779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006910742,"about_ca_topic_score_gemma":0.002805766,"domain_scores_codex":[0.9993533,0.00004214391,0.0001120542,0.0001602348,0.0001681937,0.0001640437],"domain_scores_gemma":[0.9994726,0.000311766,0.00006148747,0.0001065981,0.000007950806,0.00003962171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001017409,0.000194418,0.9576426,0.00002646293,0.00001156028,0.000002154919,0.00004498773,0.002480658,0.03433185,0.0001692996,0.003806317,0.001188003],"study_design_scores_gemma":[0.0002867007,0.0003275795,0.9351186,0.000001708555,0.00001528606,0.000007188604,0.00003013528,0.002768273,0.05618894,0.00007024638,0.005073685,0.0001116784],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892082,0.000007597066,0.0001282076,0.0001766221,0.0002674451,0.0001580711,0.00002184807,0.00003132882,0.0100007],"genre_scores_gemma":[0.9995645,0.00001246196,0.00001662712,0.00003263513,0.00003201732,0.00005265003,0.00001849846,0.000003527012,0.0002670752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06838991,"threshold_uncertainty_score":0.9320577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452986651364222,"score_gpt":0.2328538808458349,"score_spread":0.2183240143321926,"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."}}