{"id":"W4220934969","doi":"10.1101/2022.03.11.483995","title":"A connectomics-based taxonomy of mammals","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fondation Brain Canada","keywords":"Connectome; Connectomics; Phylogenetic tree; Biology; Similarity (geometry); Evolutionary biology; Computer science; Artificial intelligence; Neuroscience; Functional connectivity; Gene; Genetics","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":[0.0004407989,0.0004202704,0.0003056253,0.004174912,0.0007209933,0.001563797,0.0004881237,0.0005145543,0.00541804],"category_scores_gemma":[0.001216947,0.0001852787,0.000384933,0.002164015,0.00103727,0.001637941,0.001309568,0.0006625566,0.001221426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003791644,"about_ca_system_score_gemma":0.0004875315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001073461,"about_ca_topic_score_gemma":0.001504295,"domain_scores_codex":[0.9996809,0.00005500843,0.00003086523,0.0001500137,0.0000523808,0.00003085893],"domain_scores_gemma":[0.9994381,0.000107215,0.0001406622,0.0001307552,0.0001182599,0.00006490071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006400892,0.0001239891,0.1893845,0.001365879,0.0004612522,0.0008568568,0.005610226,0.009246438,0.2691101,0.136208,0.01755334,0.3694394],"study_design_scores_gemma":[0.00003834608,0.0003642667,0.7056638,0.0004243874,0.0001546604,0.005552666,0.002193435,0.01956494,0.00676531,0.07386795,0.1853356,0.00007467658],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6622798,0.01121278,0.2269882,0.002105572,0.0002044891,0.0005374756,0.01899993,0.00266355,0.07500821],"genre_scores_gemma":[0.7643148,0.005370151,0.2003963,0.0005882874,0.0001365056,0.0006612874,0.01926816,0.0005409875,0.008723484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00541804,"threshold_uncertainty_score":0.01812512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04473430914435552,"score_gpt":0.2379732887008418,"score_spread":0.1932389795564863,"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."}}