{"id":"W2906843401","doi":"10.1007/978-3-030-01584-8_3","title":"Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Outlier; Change detection; Geography; Statistics; Computer science; Artificial intelligence; Remote sensing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000397224,0.0001295732,0.0003244918,0.00009105293,0.00001575436,0.000006348891,0.0002717559,0.0001277927,0.0001653681],"category_scores_gemma":[0.000562181,0.00008636359,0.00002232508,0.00007392478,0.0001874077,0.00009403867,0.0001514602,0.0001660563,0.00000340943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001405271,"about_ca_system_score_gemma":0.000016511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006427598,"about_ca_topic_score_gemma":0.002129981,"domain_scores_codex":[0.9989408,0.00004138915,0.0004490764,0.0002216463,0.0002461024,0.0001010161],"domain_scores_gemma":[0.9984087,0.0008230295,0.0002043732,0.0004742869,0.00006402325,0.00002557532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008152959,0.001161274,0.009461028,0.003514293,0.0002613112,0.00004618154,0.01481687,4.011677e-7,0.0001626677,0.8222929,0.005067924,0.1423999],"study_design_scores_gemma":[0.0006098325,0.0003582175,0.01540119,0.0005148648,0.00007045521,0.000003934423,0.0002722833,0.00759189,0.000167747,0.9726875,0.002035802,0.000286229],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2155868,0.002404957,0.4205462,0.01404913,0.002495981,0.02429792,0.01295511,0.0003973304,0.3072665],"genre_scores_gemma":[0.7437233,0.0006758016,0.1941892,0.0006736784,0.000627098,0.000188818,0.0006282197,0.0001785089,0.05911531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5281365,"threshold_uncertainty_score":0.3521805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.302752542541187,"score_gpt":0.3477345207228099,"score_spread":0.04498197818162292,"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."}}