{"id":"W3209702035","doi":"10.5281/zenodo.580063","title":"Tractography Challenge ISMRM 2015 Code of the Scoring System","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Tractography; Code (set theory); Computer science; Artificial intelligence; Medicine; Diffusion MRI; Radiology; Magnetic resonance imaging; Programming language; Set (abstract data type)","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":[0.01278842,0.001950439,0.001540458,0.003604555,0.002022383,0.005938094,0.003251074,0.004035518,0.104021],"category_scores_gemma":[0.08146952,0.001463496,0.00136852,0.002400625,0.001188886,0.003732739,0.006333592,0.004520433,0.1519288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002241361,"about_ca_system_score_gemma":0.008032808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01170631,"about_ca_topic_score_gemma":0.01139342,"domain_scores_codex":[0.9853418,0.003267902,0.002063404,0.001375596,0.006825913,0.001125349],"domain_scores_gemma":[0.9341627,0.009006387,0.002278389,0.0139261,0.03760701,0.003019348],"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":[0.0003284172,0.00005124103,0.0008728287,0.0002423364,0.00002374438,0.0001388394,0.0001069012,0.001204958,0.002012052,0.004850084,0.947351,0.04281739],"study_design_scores_gemma":[0.0003093295,0.0002568634,0.00612992,0.000785853,0.00004352217,0.001328193,0.0001631964,0.01666804,0.009338128,0.02019132,0.9444651,0.0003205292],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01318653,0.001023446,0.4163365,0.01290234,0.009517159,0.003772275,0.2349119,0.230755,0.07759483],"genre_scores_gemma":[0.04228668,0.0007760352,0.2571068,0.004423101,0.002052438,0.005016489,0.4848011,0.1099981,0.09353925],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.104021,"threshold_uncertainty_score":0.3479849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1334064137899262,"score_gpt":0.3468504641227402,"score_spread":0.2134440503328141,"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."}}