{"id":"W4402311891","doi":"10.1121/10.0028500","title":"Investigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI","year":2024,"lang":"en","type":"article","venue":"JASA Express Letters","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute on Deafness and Other Communication Disorders; National Cancer Institute; National Institutes of Health","keywords":"Motion (physics); Tongue; Granger causality; Motion analysis; Muscle fibre; Dynamics (music); Biology; Diffusion; Anatomy; Computer science; Computer vision; Artificial intelligence; Biological system; Physics; Acoustics; Machine learning; Medicine","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":[0.00007988877,0.0001174776,0.0001808228,0.000195886,0.00005320629,0.00004409351,0.00004207539,0.00003751861,0.00001321195],"category_scores_gemma":[0.00002661795,0.00009685431,0.00003807952,0.0004378022,0.00006064427,0.0001490039,0.00003708163,0.0002039927,9.512914e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005531803,"about_ca_system_score_gemma":0.000007080452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006574493,"about_ca_topic_score_gemma":0.00004920075,"domain_scores_codex":[0.9991096,0.00004369703,0.0001724367,0.0003907814,0.000158441,0.0001250545],"domain_scores_gemma":[0.9995548,0.00007963892,0.00005630021,0.0002265661,0.00002485628,0.00005788703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001284849,0.00005027725,0.4389232,0.0001023426,0.00005726003,0.00004111764,0.0007985874,0.0001286288,0.5540684,0.00006152803,0.0003085404,0.005447226],"study_design_scores_gemma":[0.0004415907,0.00003258347,0.9642082,0.0005037456,0.0002918422,0.000003980808,0.00009045899,0.02362899,0.009643791,0.0003246426,0.0006589683,0.0001711789],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8373708,0.00003712461,0.1530489,0.008845816,0.00001303596,0.0004825466,0.00003322391,0.0001558461,0.00001270663],"genre_scores_gemma":[0.9895846,0.00001830494,0.00876034,0.001186001,0.00005316006,0.0002250424,0.0001372975,0.00001951745,0.00001573897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5444247,"threshold_uncertainty_score":0.3949604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03007546885721659,"score_gpt":0.3059736599162149,"score_spread":0.2758981910589984,"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."}}