{"id":"W6894362692","doi":"10.5683/sp3/ulklny","title":"3D MERMAID: 3D Multishot Enhanced Recovery Motion Artifact Insensitive Diffusion for sub-millimeter, multi-shell, and SNR efficient diffusion imaging","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artifact (error); Diffusion; Motion (physics); Diffusion imaging; Imaging technique; Signal-to-noise ratio (imaging)","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":[0.001626528,0.005677252,0.003147144,0.002472409,0.0010708,0.002362566,0.005764361,0.005035081,0.01897467],"category_scores_gemma":[0.004789357,0.001134252,0.00264883,0.003023439,0.0009481555,0.001100608,0.002187275,0.001913242,0.04154113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001489854,"about_ca_system_score_gemma":0.002437631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03584164,"about_ca_topic_score_gemma":0.08831777,"domain_scores_codex":[0.9990006,0.0001836007,0.00008632537,0.0003053998,0.0002657746,0.0001583028],"domain_scores_gemma":[0.9987715,0.0003303228,0.0001187193,0.0003370574,0.0003415099,0.0001009625],"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":[0.0006418867,0.0001383633,0.001992858,0.002652045,0.0004431944,0.0003906189,0.00006824495,0.003566824,0.001993446,0.0005629184,0.9636225,0.02392729],"study_design_scores_gemma":[0.001626084,0.0003482828,0.01328736,0.001641656,0.0009683961,0.004319879,0.0002476156,0.01363677,0.01171814,0.01032962,0.941402,0.0004743348],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003325703,0.001654065,0.00335963,0.0003125055,0.0001529404,0.0001370869,0.9836716,0.005446335,0.00194022],"genre_scores_gemma":[0.003726604,0.0004085532,0.003532109,0.0001548357,0.00003288682,0.0002414403,0.9898764,0.0004680911,0.001559091],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03584164,"threshold_uncertainty_score":0.07126606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01866105657548171,"score_gpt":0.2740954113330327,"score_spread":0.255434354757551,"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."}}