{"id":"W4404641573","doi":"10.1101/2024.11.21.24317532","title":"<i>FGF14</i> repeat length and mosaic interruptions: modifiers of SCA27B?","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Fibroblast Growth Factor Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mosaic; Computer science; Geography","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":[0.0001806295,0.0003272478,0.0002454926,0.0007736219,0.000235333,0.0001849265,0.0002070189,0.0002697416,0.001761719],"category_scores_gemma":[0.0006158327,0.0001100885,0.0002368156,0.0004529026,0.0002497154,0.0001120138,0.000198014,0.0002292032,0.0002709156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001550545,"about_ca_system_score_gemma":0.00008559142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001435829,"about_ca_topic_score_gemma":0.001403994,"domain_scores_codex":[0.9997864,0.00002866048,0.00002152918,0.00007919058,0.00005946517,0.00002469279],"domain_scores_gemma":[0.9996746,0.0000880677,0.0001237505,0.00002494469,0.00003756108,0.00005117712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001094467,0.00009586096,0.6247413,0.00006947631,0.0001432364,0.0123098,0.0003498156,0.0006785548,0.3407858,0.0002655893,0.0004107336,0.01905536],"study_design_scores_gemma":[0.00001884743,0.0002001774,0.9376618,0.00001714531,0.0001044717,0.0233415,0.0001428847,0.004015373,0.03299414,0.0002820919,0.00119995,0.00002172612],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982851,0.0001066618,0.001132168,0.00001402361,0.000002691521,0.000007012243,0.0001554519,0.00002319935,0.0002735437],"genre_scores_gemma":[0.9986104,0.0000284621,0.001024475,0.00001231229,0.000004379974,0.000004437867,0.0001291647,0.00001108918,0.0001752211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001761719,"threshold_uncertainty_score":0.005893469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182081208293134,"score_gpt":0.2988975331414218,"score_spread":0.2770767210584904,"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."}}