{"id":"W4405528262","doi":"10.1002/sta4.70007","title":"A Corrected Score Function Framework for Modelling Circadian Gene Expression","year":2024,"lang":"en","type":"article","venue":"Stat","topic":"Circadian rhythm and melatonin","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Offset (computer science); Circadian rhythm; Function (biology); Regression; Expression (computer science); Sample (material); Computer science; Sample size determination; Statistics; Transcriptome; Regression analysis; Computational biology; Biology; Data mining; Gene expression; Mathematics; Gene; Genetics; Neuroscience","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":[0.008177346,0.001608544,0.001184248,0.001981009,0.0005624308,0.001920728,0.002821153,0.001785883,0.003487283],"category_scores_gemma":[0.01621532,0.0006432309,0.001828415,0.001761598,0.001650728,0.001492843,0.001264251,0.002137045,0.00123421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002308152,"about_ca_system_score_gemma":0.002703465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0171652,"about_ca_topic_score_gemma":0.01549891,"domain_scores_codex":[0.9972156,0.001599443,0.00009201923,0.0003978771,0.0004649925,0.0002299971],"domain_scores_gemma":[0.9929116,0.004783088,0.0005451714,0.0003656242,0.001163876,0.0002306427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004829126,0.00002556712,0.001228579,0.00003923514,0.00005278291,0.00006649244,0.00004634984,0.9243509,0.0009161044,0.05624731,0.0006729821,0.01630544],"study_design_scores_gemma":[0.000002986574,0.00001444917,0.0001489992,0.000003959531,0.000004123382,0.000009020797,0.000004274929,0.9865345,0.00008484942,0.0127864,0.0003997766,0.000006666289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005323383,0.0001099899,0.9934702,0.0001203921,0.00002599401,0.00002864011,0.0001172758,0.0001814128,0.000622712],"genre_scores_gemma":[0.5536035,0.000698628,0.4283359,0.0003214084,0.0001968637,0.000715741,0.001322301,0.0006245754,0.01418119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0171652,"threshold_uncertainty_score":0.04324645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05786834505823168,"score_gpt":0.2837284382235453,"score_spread":0.2258600931653136,"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."}}