{"id":"W4391671707","doi":"10.1016/j.media.2024.103101","title":"Blurred streamlines: A novel representation to reduce redundancy in tractography","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Streamlines, streaklines, and pathlines; Redundancy (engineering); Artificial intelligence; False positive paradox; Computer science; Tractography; Representation (politics); Pattern recognition (psychology); Algorithm; Mathematics; Computer vision; Diffusion MRI","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":[],"consensus_categories":[],"category_scores_codex":[0.0002236104,0.0001277522,0.0003289476,0.0009824805,0.00003169226,0.00004528987,0.0001315022,0.00006918364,0.0003359828],"category_scores_gemma":[0.0005564688,0.0001096673,0.0002789751,0.005552656,0.00007955678,0.0001157574,0.00004407874,0.0003213747,0.00003170577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004565442,"about_ca_system_score_gemma":0.0000817579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001801056,"about_ca_topic_score_gemma":0.00004594389,"domain_scores_codex":[0.9983719,0.00002195786,0.0003998273,0.0005271797,0.0004699408,0.0002091692],"domain_scores_gemma":[0.9990501,0.0001215859,0.00003268115,0.0004471113,0.00007210945,0.0002763472],"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":[0.0002904277,0.003297006,0.04583565,0.0004285366,0.002162549,0.00279739,0.00182513,0.0002137147,0.314889,0.002240313,0.03390606,0.5921142],"study_design_scores_gemma":[0.005051679,0.0007516445,0.347748,0.00300281,0.01229272,0.0005965411,0.001576241,0.3835024,0.07628771,0.007333722,0.1597574,0.002099153],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1935573,0.0003697577,0.7624006,0.04052943,0.00006085807,0.0005351764,0.0000323161,0.0005779828,0.001936657],"genre_scores_gemma":[0.9533226,0.0001805363,0.0444768,0.001040792,0.0001545608,0.0001652506,0.0001430142,0.00002616634,0.0004902061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7597654,"threshold_uncertainty_score":0.4472104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.070228611807333,"score_gpt":0.4499603158126226,"score_spread":0.3797317040052895,"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."}}