{"id":"W2071666283","doi":"10.1016/j.neuroimage.2014.12.058","title":"A new compression format for fiber tracking datasets","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Connectomics; Diffusion MRI; Data compression; Tractography; Pipeline (software); Lossless compression; Compression (physics); JPEG 2000; Artificial intelligence; Algorithm; Image compression; Connectome; Image processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006323695,0.0001048621,0.0001472188,0.00004374127,0.00005922418,0.00002436842,0.0001074733,0.00002907955,0.00002977667],"category_scores_gemma":[0.0001058262,0.00009131915,0.00005212532,0.00009181697,0.00002117909,0.0001824267,0.00006319663,0.0001298029,0.00005005399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002094219,"about_ca_system_score_gemma":0.00004923151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008690141,"about_ca_topic_score_gemma":3.704386e-7,"domain_scores_codex":[0.9992846,0.000008985142,0.0001586316,0.000233301,0.0001338294,0.000180613],"domain_scores_gemma":[0.999191,0.00005101313,0.00005794396,0.0004302096,0.00005266281,0.0002172107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001731756,0.000100189,0.0004600314,0.00006517533,0.000004544585,0.00002475134,0.00006357661,0.00001479675,0.0179061,0.000558062,0.9121495,0.06848008],"study_design_scores_gemma":[0.001409353,0.0001793618,0.001132222,0.00004910781,0.00003625245,0.0001715661,0.000009415902,0.001733515,0.01124796,0.001548494,0.9823706,0.000112154],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03439998,0.0003209043,0.9276668,0.01393789,0.0003369814,0.004080906,0.0007350015,0.001991159,0.0165304],"genre_scores_gemma":[0.1850217,0.00004539638,0.8012225,0.005519534,0.0004764602,0.0002023383,0.001319817,0.0001537827,0.00603846],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1506217,"threshold_uncertainty_score":0.3723887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2118934394563405,"score_gpt":0.4215440450161156,"score_spread":0.2096506055597751,"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."}}