{"id":"W2526742587","doi":"10.1007/978-3-319-46720-7_21","title":"Predictive Subnetwork Extraction with Structural Priors for Infant Connectomes","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Hospital for Sick Children; University of Toronto; Child and Family Research Institute; Simon Fraser University","funders":"","keywords":"Connectome; Subnetwork; Computer science; Prior probability; Artificial intelligence; Constraint (computer-aided design); Diffusion MRI; Pattern recognition (psychology); Machine learning; Functional connectivity; Neuroscience; Mathematics; Psychology; Bayesian probability","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":[0.001069077,0.001598306,0.001209574,0.002778102,0.0005334101,0.001203195,0.001660444,0.001952523,0.004190026],"category_scores_gemma":[0.0036637,0.00102895,0.001850622,0.002015265,0.0004993656,0.001513569,0.00157172,0.002158839,0.002746804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004890568,"about_ca_system_score_gemma":0.0009485965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004245776,"about_ca_topic_score_gemma":0.01061149,"domain_scores_codex":[0.9996625,0.00007327971,0.00001729519,0.0001251124,0.00007532322,0.00004653297],"domain_scores_gemma":[0.9989091,0.0006363209,0.00008824323,0.0002123688,0.0001075928,0.00004634746],"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":[0.0003928292,0.0001449388,0.003198504,0.0003165548,0.0002341284,0.0004294965,0.0001701973,0.156836,0.02337899,0.01362879,0.01845274,0.7828168],"study_design_scores_gemma":[0.00001920051,0.00002974441,0.001640825,0.00006011842,0.00007573223,0.0002255563,0.00002569844,0.9643396,0.00542336,0.02509728,0.003045463,0.00001751212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01796332,0.001187067,0.9733462,0.0003449386,0.00005280647,0.00006727744,0.001214557,0.003745408,0.002078521],"genre_scores_gemma":[0.3580271,0.002243288,0.6176298,0.0002081096,0.0003237994,0.0003408098,0.008610574,0.001159033,0.01145744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004245776,"threshold_uncertainty_score":0.01401705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02924303732343128,"score_gpt":0.3256026376107184,"score_spread":0.2963596002872871,"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."}}