{"id":"W4404047929","doi":"10.2174/0115748936312589240910071837","title":"Robust Somatic Copy Number Estimation using Coarse-to-fine Segmentation","year":2024,"lang":"en","type":"article","venue":"Current Bioinformatics","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"AbCellera (Canada); Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Cancer Agency","funders":"Canadian Institutes of Health Research; Genome British Columbia; Canada Foundation for Innovation","keywords":"Somatic cell; Estimation; Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Biology; Genetics; Gene; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004123223,0.0001892565,0.0001846139,0.0002865451,0.0001199926,0.0007556214,0.000374544,0.000057718,0.00007285279],"category_scores_gemma":[0.00007076431,0.0001764578,0.00007760786,0.0007774553,0.00002751993,0.001884534,0.0001672871,0.0001600273,0.001480913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001756356,"about_ca_system_score_gemma":0.0001094462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003133551,"about_ca_topic_score_gemma":9.520755e-7,"domain_scores_codex":[0.9984949,0.00003818614,0.0006236259,0.0001907443,0.0003920054,0.0002605533],"domain_scores_gemma":[0.9992138,0.00009386757,0.0001269819,0.000324148,0.0001146605,0.0001265504],"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":[0.000001621224,0.00005813286,0.00007972259,0.0009286563,0.00002054632,0.000004249589,0.002803791,0.001930765,0.0002447795,0.006058736,0.008277485,0.9795915],"study_design_scores_gemma":[0.0001087018,0.00003012365,0.00001429637,0.0006358479,0.00002076153,0.00005683483,0.00004765322,0.9862492,0.008604153,0.002897407,0.001105902,0.0002291039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008928402,0.00008017739,0.9875509,0.0001961365,0.001086081,0.0004947044,0.00001566809,0.001002248,0.0006456724],"genre_scores_gemma":[0.04686384,0.00002149333,0.952768,0.0001133446,0.00006705354,0.00004646777,0.00007105462,0.00001612863,0.00003258629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9843184,"threshold_uncertainty_score":0.9992965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0595775188455197,"score_gpt":0.329495560016003,"score_spread":0.2699180411704833,"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."}}