{"id":"W4410319434","doi":"10.1101/2025.05.07.652702","title":"Mapping Functional Homologies Between Human and Marmoset Brain Networks Using Movie-Driven Ultra-High Field fMRI","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Marmoset; Neuroscience; Field (mathematics); Functional connectivity; Human brain; Artificial intelligence; Psychology; Computer science; Cartography; Biology; Geography; Mathematics; Paleontology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003168958,0.0001249943,0.0001142868,0.0004070233,0.0001920084,0.0003171648,0.0002024037,0.0002503187,0.0009351337],"category_scores_gemma":[0.0006975005,0.0001098447,0.0001844921,0.0002054969,0.0003059752,0.0002011514,0.0002352973,0.000192944,0.00007322411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374647,"about_ca_system_score_gemma":0.0001238794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001478429,"about_ca_topic_score_gemma":0.00201904,"domain_scores_codex":[0.9999094,0.00002876911,0.000004148142,0.00003101836,0.00001215677,0.00001441663],"domain_scores_gemma":[0.9998872,0.00003389101,0.00002649341,0.00002300995,0.00001300487,0.00001638514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005300029,0.00007813608,0.03249975,0.00015673,0.0002447148,0.000441734,0.000515794,0.00825478,0.9077778,0.004279408,0.0006126883,0.04460851],"study_design_scores_gemma":[0.00003115661,0.0002100467,0.7355263,0.00004158779,0.0001548084,0.001972293,0.0003879162,0.1231029,0.124248,0.01077471,0.003500469,0.00004975864],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747537,0.0001453567,0.02400153,0.00008334003,0.000007434124,0.00001106154,0.0003097512,0.00009569216,0.0005920411],"genre_scores_gemma":[0.9842229,0.00005620337,0.01517037,0.00002129201,0.000007299165,0.00001806933,0.0003056909,0.00001707113,0.0001810551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001478429,"threshold_uncertainty_score":0.00312835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515703150181504,"score_gpt":0.2462647460900564,"score_spread":0.2311077145882413,"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."}}