{"id":"W6907462999","doi":"10.21227/tjv6-cf92","title":"Ultrasound Beamforming using MobileNetV2","year":2020,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deep learning; Beamforming; Preprocessor; Channel (broadcasting); Image quality; Image (mathematics); Autoencoder; Transformation (genetics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006408234,0.001072831,0.001176368,0.0004253323,0.0002907589,0.0002933393,0.002259417,0.0006702104,0.001139982],"category_scores_gemma":[0.000405705,0.00115858,0.0002749745,0.0008436022,0.0002408989,0.0007944813,0.0004296769,0.001463741,0.04597288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004712044,"about_ca_system_score_gemma":0.0008150974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001714781,"about_ca_topic_score_gemma":0.0002514316,"domain_scores_codex":[0.9947212,0.0001042589,0.001140514,0.001617649,0.001350435,0.001065968],"domain_scores_gemma":[0.994934,0.0001781999,0.001005422,0.003214805,0.000125529,0.0005420545],"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":[0.00003771418,0.0001036591,0.00002158233,0.0002686336,0.0002472077,0.0006243001,0.00001802199,0.0002138791,0.005869046,5.637199e-7,0.9925625,0.00003292163],"study_design_scores_gemma":[0.0003288479,0.00004865557,0.000009925685,0.0002099784,0.0007058297,0.0008605543,0.00003528135,0.00006040147,0.0009474008,0.00001067581,0.9955525,0.001229922],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000347976,0.00009754553,0.0001048847,0.000004310137,0.00224201,0.0007616658,0.9960661,0.0003024038,0.00007309137],"genre_scores_gemma":[0.00002236451,0.0001277776,0.001292225,0.0004307128,0.002404974,0.00005371809,0.9952877,0.000329928,0.00005056],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0448329,"threshold_uncertainty_score":0.9997731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05929576673812678,"score_gpt":0.3193351527469321,"score_spread":0.2600393860088053,"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."}}