{"id":"W4403600544","doi":"10.1109/access.2024.3484270","title":"Efficient Observation Time Window Segmentation for Administrative Data Machine Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Window (computing); Segmentation; Artificial intelligence; Machine learning; Operating system","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":[0.0002261544,0.00007933412,0.00006742773,0.00006627367,0.0001741715,0.0005093375,0.0008425167,0.00003350738,0.00001617157],"category_scores_gemma":[0.00001748658,0.00007365051,0.00002802541,0.0003901455,0.00001485007,0.0005841238,0.000148479,0.00008794133,0.00004044819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003629072,"about_ca_system_score_gemma":0.0000515059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002109234,"about_ca_topic_score_gemma":0.000004476958,"domain_scores_codex":[0.9992121,0.00002274658,0.000155182,0.00037912,0.0001222208,0.0001086089],"domain_scores_gemma":[0.9993663,0.0001113678,0.00005896528,0.0003764151,0.00005531671,0.00003166758],"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":[0.00004953563,0.0003728122,0.0009868492,0.0003190177,0.0001766109,0.00001472029,0.001535676,0.02622945,0.1446164,0.06275578,0.02354589,0.7393973],"study_design_scores_gemma":[0.00006786584,0.00005611587,0.0003101297,0.00001622352,0.000008837136,0.000003556985,0.000005387626,0.9529202,0.03381507,0.0009190321,0.0117841,0.0000934601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01547671,0.00007639393,0.9822102,0.0008277538,0.0001678999,0.0004335851,0.00004611013,0.0005292179,0.0002321252],"genre_scores_gemma":[0.9459368,0.000009769436,0.0523691,0.0001449879,0.0001286248,0.0002275343,0.0001853146,0.00001255076,0.000985294],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9304601,"threshold_uncertainty_score":0.4911553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1257977195144193,"score_gpt":0.3915598023675483,"score_spread":0.265762082853129,"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."}}