{"id":"W1983676139","doi":"10.1034/j.1399-0004.2001.600102.1.x","title":"A super sensor for DNA integrity: bigger is better","year":2001,"lang":"en","type":"article","venue":"Clinical Genetics","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Library science; Medical genetics; Medicine; Gerontology; Computer science; Genetics; Biology","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":[0.0003170944,0.0001393648,0.000337662,0.00003469696,0.00004398394,0.0000193233,0.0001049666,0.0002928091,0.0002049698],"category_scores_gemma":[0.002247362,0.0001110304,0.0002541058,0.00009733612,0.0001378782,0.00001568052,0.00006863847,0.0003651133,0.0002304381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001280292,"about_ca_system_score_gemma":0.0000618882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007450461,"about_ca_topic_score_gemma":0.000004383679,"domain_scores_codex":[0.9986858,0.00003759248,0.000483091,0.0003161737,0.0001748882,0.0003024133],"domain_scores_gemma":[0.99785,0.001229124,0.0000485838,0.0004130013,0.0001596316,0.0002996411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003747643,0.0004358207,0.7734498,0.00004295617,0.0001323436,0.00008160626,0.00008777931,0.000001580136,0.0002404287,0.00004088457,0.08992845,0.1351836],"study_design_scores_gemma":[0.00371019,0.001512121,0.2822453,0.0001321778,0.0004319981,0.00007778431,0.00006049002,0.002274568,0.002506413,0.0008688222,0.7058775,0.000302569],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795708,0.0004779915,0.006263221,0.01062901,0.0004743701,0.0003580007,0.0000523387,0.0000742519,0.002100008],"genre_scores_gemma":[0.892103,0.001051436,0.06057037,0.03806577,0.002714349,0.00003089906,0.0000782201,0.00006094061,0.005325059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6159491,"threshold_uncertainty_score":0.4527688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139059923118061,"score_gpt":0.4134209934022182,"score_spread":0.2743610702841572,"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."}}