{"id":"W2152621450","doi":"10.5194/npg-13-339-2006","title":"Time series segmentation with shifting means hidden markov models","year":2006,"lang":"en","type":"article","venue":"Nonlinear processes in geophysics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Markov chain; Markov chain Monte Carlo; Hidden Markov model; Computer science; Variable-order Markov model; Series (stratigraphy); Markov model; Hidden semi-Markov model; Markov property; Maximum-entropy Markov model; Bayesian probability; State space; Algorithm; Artificial intelligence; Mathematics; Machine learning; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001613842,0.0005603093,0.0008277686,0.0009926099,0.0004406359,0.0008848892,0.001088114,0.001115438,0.00171709],"category_scores_gemma":[0.005489352,0.0004984144,0.001159681,0.0009651171,0.001005501,0.001801626,0.000981616,0.001474573,0.0005162521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115905,"about_ca_system_score_gemma":0.0007486031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004971512,"about_ca_topic_score_gemma":0.003869139,"domain_scores_codex":[0.999228,0.0002845748,0.00003702107,0.0002264265,0.0001583298,0.00006563541],"domain_scores_gemma":[0.9977269,0.001611476,0.0002334714,0.0001872012,0.0001781947,0.00006279825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001398899,0.00004943981,0.001980014,0.00007075093,0.0001117858,0.0001737273,0.0002135998,0.8497404,0.003453493,0.0669505,0.001504565,0.07561173],"study_design_scores_gemma":[0.000002818203,0.0000050599,0.0001439871,0.000005526359,0.000005161233,0.00001331828,0.000005009982,0.9833634,0.000518657,0.01555181,0.0003786668,0.000006583017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01637526,0.000214833,0.9820954,0.0001333343,0.00003380614,0.00001760726,0.00007139207,0.0003363254,0.0007219969],"genre_scores_gemma":[0.624434,0.0004432186,0.3711767,0.0001607702,0.0001034241,0.0001524442,0.0005500859,0.0001883142,0.002791053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004971512,"threshold_uncertainty_score":0.009885132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007745357783791959,"score_gpt":0.2053329759562338,"score_spread":0.1975876181724418,"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."}}