{"id":"W4207033664","doi":"10.1016/j.dib.2022.107863","title":"The PaleoArchiNeo (PAN) human brain atlas: A dataset on a standard neuroanatomical MRI template following a phylogenetic approach","year":2022,"lang":"en","type":"article","venue":"Data in Brief","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Atlas (anatomy); Brain atlas; Phylogenetic tree; Human brain; Research article; Computer science; Biology; Artificial intelligence; Anatomy; Neuroscience; Library science","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":[0.0005224387,0.001105395,0.0007795673,0.002610942,0.0005185427,0.001062818,0.001436371,0.0008897514,0.02386479],"category_scores_gemma":[0.001792937,0.0003840752,0.0007663238,0.003843889,0.0003605633,0.0007885105,0.00158224,0.0008290246,0.01581396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006882735,"about_ca_system_score_gemma":0.001387704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01311915,"about_ca_topic_score_gemma":0.03671004,"domain_scores_codex":[0.9997213,0.00004635694,0.00003460187,0.0001127119,0.0000550685,0.00002988918],"domain_scores_gemma":[0.9996344,0.00009389762,0.00004085086,0.0001035831,0.00009466332,0.00003262964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004474281,0.00006689605,0.006350911,0.003051307,0.0003092558,0.0008338765,0.0006828734,0.002507488,0.005494292,0.005772537,0.867147,0.1073361],"study_design_scores_gemma":[0.0001261073,0.00004605843,0.03350272,0.0004454587,0.0001411107,0.002202911,0.0003560774,0.001857704,0.001663026,0.00672435,0.9528653,0.00006918543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01507418,0.00238056,0.01218484,0.0003885266,0.0001595298,0.0002890712,0.9564112,0.003393751,0.009718388],"genre_scores_gemma":[0.03094786,0.001591825,0.02575348,0.0001904912,0.00007975058,0.001614689,0.9342356,0.0008516083,0.004734746],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02386479,"threshold_uncertainty_score":0.07983565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05945073048854336,"score_gpt":0.3144586073332247,"score_spread":0.2550078768446813,"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."}}