{"id":"W4414260689","doi":"10.1101/2025.09.09.675160","title":"netneurotools: a trainee-oriented approach to network neuroscience","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Workaround; Neuroinformatics; Software; Field (mathematics); Python (programming language); Pipeline (software); Heuristics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005618735,0.000661101,0.0005863222,0.000273514,0.0004342434,0.0007527436,0.003634459,0.0003157464,0.000002381827],"category_scores_gemma":[0.000135541,0.0006966025,0.0002060877,0.003456154,0.0001051957,0.0002903504,0.002818093,0.001002074,0.00003100599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200381,"about_ca_system_score_gemma":0.0005626618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001723497,"about_ca_topic_score_gemma":4.941238e-7,"domain_scores_codex":[0.9952826,0.0001731745,0.0006359283,0.002293259,0.0005642871,0.00105072],"domain_scores_gemma":[0.9955776,0.00009513438,0.0003085672,0.003115672,0.0003535481,0.000549496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004559678,0.001460221,0.001555606,0.0007657962,0.0001317193,0.0001188335,0.00007789097,0.1991473,0.2040921,0.5369519,0.05551471,0.0001383768],"study_design_scores_gemma":[0.0008850949,0.000207734,0.04478866,0.0008504641,0.0001263399,1.499656e-7,0.000001776147,0.5328962,0.01466572,0.0001050084,0.4018545,0.003618303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01367611,0.0002906563,0.977273,0.00164147,0.002781386,0.002528558,0.0001272219,0.001437705,0.0002438799],"genre_scores_gemma":[0.8072655,0.0001124049,0.1816757,0.007340479,0.001203845,0.002232499,4.429825e-7,0.00008973567,0.00007935495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7955973,"threshold_uncertainty_score":0.9995485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02113911648639796,"score_gpt":0.2338084384324431,"score_spread":0.2126693219460452,"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."}}