{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003313407,0.0002067328,0.000195778,0.0001084981,0.0001568159,0.0001697555,0.0005352186,0.0002645839,0.0000143358],"category_scores_gemma":[0.001022188,0.0002298643,0.00005365186,0.0001074283,0.0001159601,0.00001773319,0.0008473307,0.0002800185,0.000001690674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006507451,"about_ca_system_score_gemma":0.000325458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00020496,"about_ca_topic_score_gemma":0.00105617,"domain_scores_codex":[0.9970195,0.001455574,0.0003444683,0.0007917645,0.0001918297,0.0001968631],"domain_scores_gemma":[0.9974111,0.0001237817,0.0002909339,0.001110717,0.0009535284,0.0001099589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001839398,0.0002994272,0.002325171,0.0002588234,0.00007725695,9.6729e-7,0.003072261,0.001176423,0.8812525,0.0006944922,0.001549115,0.1091097],"study_design_scores_gemma":[0.004767646,0.000009514564,0.008283655,0.0009637828,0.0001143678,0.00002257428,0.002384948,0.2904485,0.6389692,0.0008204503,0.05213795,0.001077365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6487191,0.001618832,0.3433231,0.001840436,0.0001935083,0.0009869776,0.0001580081,0.00005711065,0.0031029],"genre_scores_gemma":[0.9769683,0.0007288522,0.01465746,0.00004232992,0.00004352385,0.0001513362,0.006063671,0.00003119269,0.001313334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3286657,"threshold_uncertainty_score":0.9373592,"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."}}