{"id":"W1599201937","doi":"","title":"PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Benchmark (surveying); Unsupervised learning; Pattern recognition (psychology); Inference; Supervised learning; Machine learning; Artificial neural network","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":[0.003851134,0.002408505,0.002695087,0.002805822,0.001154425,0.002826211,0.005516892,0.00369771,0.007403599],"category_scores_gemma":[0.0115117,0.001895156,0.002223768,0.003106333,0.001752743,0.002884368,0.004540313,0.003884163,0.004590243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066426,"about_ca_system_score_gemma":0.0028973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00660187,"about_ca_topic_score_gemma":0.009797537,"domain_scores_codex":[0.9978542,0.0006408194,0.0001033532,0.0006237099,0.0005617186,0.0002162179],"domain_scores_gemma":[0.9961948,0.001931336,0.0002218993,0.0008431409,0.0005334212,0.0002752933],"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":[0.001869701,0.0005846902,0.002893809,0.0006111026,0.00039863,0.0003014684,0.0002446243,0.2451016,0.02363971,0.01458034,0.04642573,0.6633486],"study_design_scores_gemma":[0.00006557527,0.00004235519,0.0004308044,0.00001515919,0.00001391195,0.00005399413,0.00002032848,0.9790222,0.004627871,0.01334578,0.002336323,0.00002564348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008648518,0.0002284313,0.9744422,0.0002779264,0.00008696889,0.00009848017,0.0008665507,0.01488605,0.000464847],"genre_scores_gemma":[0.08422551,0.0001806336,0.8996559,0.0002899223,0.0001541633,0.0003496622,0.005844856,0.004821254,0.004478078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007403599,"threshold_uncertainty_score":0.02476758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03143131004959567,"score_gpt":0.2770110710355999,"score_spread":0.2455797609860042,"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."}}