{"id":"W2755420070","doi":"10.1101/188706","title":"A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Defense Advanced Research Projects Agency; National Natural Science Foundation of China; Kavli Foundation; Advanced Research Projects Agency; National Institutes of Health; National Science Foundation","keywords":"Connectome; Connectomics; Computer science; Pipeline (software); Replicate; Data mining; Pooling; Pipeline transport; Artificial intelligence; Statistics; Functional connectivity; Biology; Mathematics; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002174952,0.001209679,0.001532366,0.0004158705,0.001362114,0.001164753,0.002035863,0.0007942385,0.00015613],"category_scores_gemma":[0.04101066,0.00127702,0.0004392692,0.0004767642,0.001066035,0.0007837981,0.00289498,0.001641799,0.0003594448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006964178,"about_ca_system_score_gemma":0.000938537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004276699,"about_ca_topic_score_gemma":0.00002540328,"domain_scores_codex":[0.992389,0.0007470612,0.001016281,0.003535684,0.001171168,0.001140862],"domain_scores_gemma":[0.9887941,0.00440894,0.001207056,0.004334115,0.0008967377,0.000359075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001886501,0.0006073955,0.004147398,0.0009533774,0.0002314159,0.0002460787,0.00002562247,0.001009852,0.9742284,0.01038142,0.007977436,0.000002971034],"study_design_scores_gemma":[0.00167847,0.0001078015,0.05495041,0.0005275246,0.0002910554,1.72862e-7,0.00000751002,0.001135815,0.9302641,0.0003900943,0.008360433,0.002286674],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695717,0.0005679158,0.006884467,0.005998695,0.01151568,0.001876908,0.001808076,0.001685594,0.00009095034],"genre_scores_gemma":[0.993632,0.0003395521,0.00216266,0.001260935,0.00171364,0.0005429531,5.606394e-7,0.0002141304,0.0001335528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05080301,"threshold_uncertainty_score":0.999938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04059626832978613,"score_gpt":0.2492583547995526,"score_spread":0.2086620864697664,"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."}}