{"id":"W4414870063","doi":"10.1101/2025.10.05.680511","title":"Mixture Models for Domain-Adaptive Brain Decoding","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoding methods; Mixture model; Weighting; Scalability; Selection (genetic algorithm); Generalization; Inference; Model selection","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009752784,0.0006456862,0.0007097488,0.0005310917,0.0003200892,0.0005955372,0.001957773,0.0007096385,0.0000204219],"category_scores_gemma":[0.0003346967,0.00070035,0.0004055188,0.0006640483,0.0000750984,0.0004486086,0.001045572,0.0006454499,0.00003492003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000329992,"about_ca_system_score_gemma":0.0009143908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001493349,"about_ca_topic_score_gemma":0.000003161145,"domain_scores_codex":[0.9964951,0.0001928132,0.0005881373,0.001572551,0.0004322222,0.0007192129],"domain_scores_gemma":[0.9962681,0.000568172,0.0003921653,0.00172194,0.0007570588,0.0002925935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000206387,0.0008171841,0.0002303466,0.001990911,0.00160589,0.0002459777,0.0002625849,0.001173083,0.1686569,0.7908058,0.03295219,0.001052761],"study_design_scores_gemma":[0.002799137,0.0002031217,0.001401043,0.003002575,0.0002976426,1.06824e-7,0.00002181863,0.4560038,0.4721365,0.00763583,0.05172053,0.0047779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007002473,0.0005418489,0.985461,0.002236605,0.001934527,0.001272496,0.0004300803,0.0008921946,0.0002287737],"genre_scores_gemma":[0.2490947,0.00008835316,0.7472952,0.002180374,0.0004218848,0.0007889775,3.539354e-7,0.00007457963,0.00005556584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.78317,"threshold_uncertainty_score":0.9995447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02739200906166949,"score_gpt":0.2393432915219917,"score_spread":0.2119512824603222,"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."}}