{"id":"W4402973064","doi":"10.1101/2024.09.27.615384","title":"Activation mapping in multi-center rat sensory-evoked functional MRI datasets using a unified pipeline","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Douglas Mental Health University Institute","funders":"Biotechnology and Biological Sciences Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Pipeline (software); Sensory system; Center (category theory); Computer science; Neuroscience; Artificial intelligence; Chemistry; Psychology","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":[0.003209862,0.001273476,0.0008856045,0.002224182,0.0006265097,0.001311419,0.001202303,0.0006901707,0.003037913],"category_scores_gemma":[0.005766706,0.0004841517,0.001612142,0.001389537,0.0004392003,0.0007320215,0.001473841,0.001189827,0.001546759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005977531,"about_ca_system_score_gemma":0.00181666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004537148,"about_ca_topic_score_gemma":0.00861769,"domain_scores_codex":[0.9992418,0.000166463,0.0000731856,0.0003065022,0.0001370671,0.00007497276],"domain_scores_gemma":[0.9989384,0.0003359418,0.0001209554,0.00026167,0.0002850161,0.00005801581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003115782,0.000778693,0.02575921,0.001959592,0.002523651,0.0008945402,0.001237166,0.1153884,0.3569573,0.008441409,0.06783346,0.4151108],"study_design_scores_gemma":[0.0003479512,0.000635737,0.06726094,0.0001609576,0.0007075995,0.0006699254,0.0003173132,0.7649546,0.1144414,0.01683123,0.03337232,0.0002999978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.147605,0.0005003158,0.7561496,0.0003977799,0.0001560822,0.0005519948,0.02200749,0.07133936,0.00129228],"genre_scores_gemma":[0.2839043,0.0003137527,0.6626472,0.0002075459,0.00005027595,0.002488849,0.04266606,0.006194504,0.001527378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004537148,"threshold_uncertainty_score":0.01697558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05104491617280692,"score_gpt":0.2605178023660792,"score_spread":0.2094728861932723,"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."}}