{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002954905,0.001322306,0.0004763636,0.0007216485,0.0002249766,0.000699003,0.001307527,0.0008840541,0.007999789],"category_scores_gemma":[0.0009756386,0.0003609562,0.0004294947,0.0004505309,0.000227725,0.0008461359,0.001132872,0.0007305782,0.003267092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006122488,"about_ca_system_score_gemma":0.0006914631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007330793,"about_ca_topic_score_gemma":0.009695158,"domain_scores_codex":[0.9998615,0.00002240386,0.000008017696,0.00003204028,0.00004405496,0.00003195828],"domain_scores_gemma":[0.9998572,0.00003957504,0.0000136991,0.00002058133,0.00004923794,0.00001965807],"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":[0.0009753552,0.0003226631,0.003184725,0.0004165244,0.0002540005,0.0006070653,0.0001162227,0.4596432,0.01720243,0.009656351,0.06116221,0.4464592],"study_design_scores_gemma":[0.0000483828,0.00008196393,0.0001873297,0.00002204709,0.00001287953,0.0000612193,0.00001969746,0.9834568,0.005681826,0.003313394,0.007097103,0.00001728693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.06779493,0.002811477,0.8118044,0.001416602,0.001006959,0.0003578069,0.004097183,0.08476189,0.02594886],"genre_scores_gemma":[0.7145562,0.001204963,0.2513837,0.001255541,0.0001592724,0.0007568713,0.01156224,0.002173513,0.01694764],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.007999789,"threshold_uncertainty_score":0.02676195,"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."}}