{"id":"W1973855283","doi":"10.1111/j.0906-7590.2008.05548.x","title":"Detecting spatial hot spots in landscape ecology","year":2008,"lang":"en","type":"article","venue":"Ecography","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":220,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Victoria","funders":"Natural Resources Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada","keywords":"Cold spot; Spatial ecology; Hot spot (computer programming); Spatial analysis; Spots; Ecology; Estimator; Common spatial pattern; Geography; Cartography; Statistics; Physical geography; Remote sensing; Biology; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004344277,0.0003690467,0.000631674,0.003657656,0.0006251247,0.001383754,0.0005865264,0.000763984,0.001175853],"category_scores_gemma":[0.02731584,0.0003775963,0.0004907075,0.003493704,0.001618184,0.002243998,0.001597282,0.0007008077,0.0001759005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006810945,"about_ca_system_score_gemma":0.000379712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002945657,"about_ca_topic_score_gemma":0.003725864,"domain_scores_codex":[0.99794,0.001219008,0.0001099437,0.0003949749,0.0002396734,0.00009632781],"domain_scores_gemma":[0.9661995,0.02639925,0.003929467,0.002114182,0.0009594371,0.0003981137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000420404,0.0001533639,0.5602365,0.0007392621,0.0006627136,0.0007143678,0.002849192,0.1420653,0.00911598,0.06162566,0.002283463,0.2191338],"study_design_scores_gemma":[0.00002911628,0.0001935821,0.4972317,0.000144932,0.0001582032,0.000903523,0.001916492,0.3414047,0.006347256,0.1476986,0.003845167,0.0001268014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6483361,0.001686763,0.3460713,0.0003110824,0.00002080562,0.00008756347,0.0004676617,0.0002905662,0.00272823],"genre_scores_gemma":[0.9654067,0.0002261668,0.03381129,0.00002307938,0.00002018633,0.00003826869,0.0002158634,0.00001658621,0.0002418644],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004344277,"threshold_uncertainty_score":0.02297497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02979604630033603,"score_gpt":0.195654194914632,"score_spread":0.165858148614296,"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."}}