{"id":"W2003315067","doi":"10.1890/0012-9658(2006)87[2603:smietf]2.0.co;2","title":"SPATIAL MODELING IN ECOLOGY: THE FLEXIBILITY OF EIGENFUNCTION SPATIAL ANALYSES","year":2006,"lang":"en","type":"article","venue":"Ecology","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":636,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spatial analysis; Eigenfunction; Flexibility (engineering); Ecology; Spatial ecology; Computer science; Range (aeronautics); Eigenvalues and eigenvectors; Mathematics; Topology (electrical circuits); Mathematical optimization; Statistics; Biology; Engineering","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":[0.0003341915,0.00007175012,0.0001607404,0.00002868333,0.00007143088,0.000004820209,0.0001419077,0.00008999383,0.001748475],"category_scores_gemma":[0.00001418995,0.00004701691,0.00004135705,0.0001122407,0.00002961225,0.0000688329,0.00009067864,0.00007180212,0.0001425751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008824163,"about_ca_system_score_gemma":0.00001119808,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05245512,"about_ca_topic_score_gemma":0.6181362,"domain_scores_codex":[0.9991421,0.0001191814,0.000273564,0.000195792,0.00008209787,0.0001872986],"domain_scores_gemma":[0.9996569,0.00006789355,0.00008235322,0.0001712442,0.000005953447,0.00001563382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002416123,0.0000666095,0.735651,0.000004802636,0.000004516786,0.000001599574,0.00005595843,0.2631294,0.0004983311,0.000007006789,0.00003632747,0.0005202774],"study_design_scores_gemma":[0.0001735274,0.0000537425,0.6576937,0.000001146627,0.00001033409,0.000001362989,0.0000334484,0.3402169,0.0004564461,0.001254095,0.00006184047,0.00004341575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996531,0.00001310908,0.0006112132,0.0001765628,0.0002504498,0.0001365283,0.00000274161,0.00001134256,0.00226707],"genre_scores_gemma":[0.9997504,0.000003560977,0.00004183128,0.00007317037,0.00008281688,0.00001515567,0.000008433331,0.000003896253,0.0000207865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.565681,"threshold_uncertainty_score":0.999164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03274840353725991,"score_gpt":0.2674052946257218,"score_spread":0.2346568910884619,"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."}}