{"id":"W3033528306","doi":"10.1101/2020.06.04.134635","title":"A generative modeling approach for interpreting population-level variability in brain structure","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institutes of Health; National Science Foundation","keywords":"Generative grammar; Autoencoder; Bottleneck; Generative model; Computer science; Artificial intelligence; Variation (astronomy); Artificial neural network; Latent variable; Machine learning; Coupling (piping); Population; Physics","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":[0.001246993,0.0005558517,0.0004061539,0.0006549029,0.0002762593,0.0007080653,0.001047218,0.0009269207,0.001244268],"category_scores_gemma":[0.002979605,0.0005863701,0.001073545,0.0004885943,0.001095329,0.0007793074,0.001146159,0.001510523,0.0001911112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009978637,"about_ca_system_score_gemma":0.0006919318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006383155,"about_ca_topic_score_gemma":0.007392565,"domain_scores_codex":[0.9997422,0.0001194542,0.000008694159,0.00006369193,0.00003793037,0.00002804981],"domain_scores_gemma":[0.9992141,0.0005210104,0.00008733752,0.00008640715,0.00005040455,0.00004086025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002419259,0.00001417026,0.001614819,0.00001909114,0.0000734872,0.00006317587,0.00008886255,0.9435154,0.004235903,0.04115386,0.0005499032,0.008647294],"study_design_scores_gemma":[0.000001678177,0.000003537715,0.0002530407,0.000003285337,0.00000451324,0.00001171893,0.000005162763,0.9839345,0.0002422206,0.01534821,0.0001879124,0.000004323073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02303482,0.0001451579,0.9753494,0.0003832827,0.0000180948,0.00001332234,0.0001239225,0.000199395,0.0007325528],"genre_scores_gemma":[0.8784351,0.0004171849,0.1169181,0.0003042542,0.00007738244,0.0001342983,0.0003624396,0.0002182723,0.003133006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006383155,"threshold_uncertainty_score":0.01269197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0448372389415946,"score_gpt":0.2531265014146746,"score_spread":0.20828926247308,"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."}}