{"id":"W4392589489","doi":"10.1016/j.gimo.2024.101533","title":"P627: Pool of normal optimization for NGS-based CNV calling in a clinical setting","year":2024,"lang":"en","type":"article","venue":"Genetics in Medicine Open","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute","funders":"","keywords":"Copy-number variation; Computational biology; Computer science; Biology; Genetics; Genome; Gene","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":[0.003997128,0.001956722,0.001244133,0.002318143,0.000998743,0.002301155,0.001150379,0.001385339,0.02004856],"category_scores_gemma":[0.01265811,0.0008250079,0.000861872,0.001354967,0.0006243063,0.001172265,0.002099182,0.001024036,0.01287783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005537402,"about_ca_system_score_gemma":0.001526667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002680968,"about_ca_topic_score_gemma":0.003042489,"domain_scores_codex":[0.997376,0.0003677059,0.0002284118,0.001031766,0.0008046392,0.000191488],"domain_scores_gemma":[0.9975947,0.0008240966,0.0001420547,0.0005429668,0.0007333502,0.0001627132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004055227,0.0004210813,0.04157583,0.000961242,0.0004357558,0.002058207,0.001147537,0.01080152,0.223231,0.005339534,0.0571091,0.652864],"study_design_scores_gemma":[0.0006892198,0.001524245,0.08994022,0.0005305423,0.0009240269,0.009692848,0.0006141883,0.1526496,0.4575531,0.02570242,0.2596546,0.0005250468],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.255529,0.0052408,0.6004792,0.001447878,0.0006979311,0.001557368,0.03373206,0.06956771,0.03174794],"genre_scores_gemma":[0.3761141,0.00135221,0.5313687,0.001105411,0.0002739955,0.003429586,0.05218604,0.01568294,0.01848701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02004856,"threshold_uncertainty_score":0.06706911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09972710604639277,"score_gpt":0.4784513207486973,"score_spread":0.3787242147023046,"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."}}