{"id":"W2032490573","doi":"10.1080/13658810110060442","title":"Detecting outliers in irregularly distributed spatial data sets by locally adaptive and robust statistical analysis and GIS","year":2001,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo","keywords":"Outlier; Computer science; Data mining; Anomaly detection; Sorting; Exploratory data analysis; Set (abstract data type); Spatial analysis; Data set; Block (permutation group theory); Artificial intelligence; Geography; Mathematics; Algorithm; Remote sensing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003792778,0.0007021122,0.001397338,0.005028392,0.0005405917,0.001681871,0.001580231,0.0008842554,0.0007548652],"category_scores_gemma":[0.01871096,0.000464331,0.001066409,0.00408191,0.001590889,0.002003335,0.001667789,0.00112451,0.0004414625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007615308,"about_ca_system_score_gemma":0.001048986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002338422,"about_ca_topic_score_gemma":0.001855682,"domain_scores_codex":[0.9955897,0.001455875,0.0004640072,0.0006922717,0.001649233,0.0001488688],"domain_scores_gemma":[0.9836039,0.008282688,0.003350046,0.00199934,0.002490375,0.000273544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000412765,0.0002109068,0.03889549,0.000406668,0.0004746657,0.0006772351,0.0007732438,0.4075882,0.03305298,0.03225819,0.002584268,0.4826654],"study_design_scores_gemma":[0.00001943206,0.00007428363,0.004447314,0.0000214433,0.00004304011,0.00029176,0.0001416346,0.9630095,0.01033281,0.01937536,0.002180048,0.00006335512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01376302,0.00005517771,0.9853299,0.0000626901,0.000009578112,0.00002367034,0.00004201912,0.0005566178,0.0001572625],"genre_scores_gemma":[0.2417878,0.00008413121,0.7571439,0.00004808633,0.00004235065,0.0001026681,0.000277919,0.000137221,0.0003758938],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005028392,"threshold_uncertainty_score":0.02005839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01631648945668758,"score_gpt":0.262923315434998,"score_spread":0.2466068259783105,"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."}}