{"id":"W2991263362","doi":"10.1121/1.5133665","title":"Domain adaptation for ultrasound tongue contour extraction using transfer learning: A deep learning approach","year":2019,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Domain adaptation; Tongue; Transfer of learning; Adaptation (eye); Task (project management); Pattern recognition (psychology); Convolutional neural network; Deep learning; Domain (mathematical analysis); Extraction (chemistry); Computer vision; Speech recognition; Mathematics","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":[0.0008311036,0.000142475,0.0004477484,0.00002507075,0.0002333181,0.000007504065,0.000144404,0.00006289856,0.0000313794],"category_scores_gemma":[0.0004819173,0.00007378441,0.0004776519,0.0001976758,0.0003484051,0.00008469376,0.00001655998,0.0007827113,0.000001114927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001105401,"about_ca_system_score_gemma":0.00009263277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002867851,"about_ca_topic_score_gemma":3.18586e-7,"domain_scores_codex":[0.9985343,0.0001740875,0.000427667,0.00009919942,0.0005711655,0.0001935569],"domain_scores_gemma":[0.9977496,0.001427016,0.000310777,0.0001166616,0.0003271691,0.00006882375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002235879,0.0004776321,0.004879615,0.0005089263,0.001352213,0.000001368785,0.01618817,0.4891041,0.4797818,0.0000635038,0.001018474,0.004388268],"study_design_scores_gemma":[0.006592042,0.003499659,0.04960018,0.0005425806,0.003121414,0.001560806,0.0955275,0.8315983,0.001159734,0.001249082,0.005229765,0.0003189002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3539024,0.0002874636,0.6436595,0.001531597,0.0001312599,0.00030581,0.000001851675,0.000007542628,0.0001725477],"genre_scores_gemma":[0.9403861,0.0001761057,0.05846186,0.0003643071,0.0004514899,0.000002557477,0.0000029182,0.00002202633,0.0001326697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5864837,"threshold_uncertainty_score":0.3400534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569264029141682,"score_gpt":0.2862359921951272,"score_spread":0.2605433519037104,"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."}}