{"id":"W3131458709","doi":"10.1155/2021/3260259","title":"Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention","year":2021,"lang":"en","type":"article","venue":"Complexity","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"China Scholarship Council; National Natural Science Foundation of China; Natural Science Foundation of Chongqing; National Science Foundation","keywords":"Overfitting; Computer science; Encoder; Segmentation; Artificial intelligence; Frame (networking); Pattern recognition (psychology); Sequence (biology); Process (computing); Pixel; Computer vision; Artificial neural network","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":[0.0001546053,0.00009662458,0.0001990675,0.00005400226,0.0001475391,0.00002904019,0.00002377119,0.00004660729,0.0001091133],"category_scores_gemma":[0.00004835845,0.00007872613,0.0001267268,0.0001012043,0.00008250876,0.00004695009,0.00001275954,0.00007640391,0.00001536108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000368401,"about_ca_system_score_gemma":0.0000690911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004252233,"about_ca_topic_score_gemma":0.0002874013,"domain_scores_codex":[0.9992336,0.00004877059,0.0001409106,0.0002757368,0.0001695563,0.0001313988],"domain_scores_gemma":[0.9993653,0.00004639138,0.00005877295,0.0002482301,0.0001912142,0.00009010004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009405126,0.0008854963,0.8950114,0.0006240113,0.0006522023,0.00006694254,0.000385115,0.0001799784,0.07060944,0.0002477685,0.001528143,0.02886903],"study_design_scores_gemma":[0.003107005,0.0001019763,0.9481314,0.00004128823,0.0001919161,0.000111276,0.000120116,0.02598661,0.01031708,0.00007007974,0.01169531,0.0001259413],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7090786,0.0002429371,0.2895963,0.0004765056,0.0001501517,0.0003346681,0.00001821687,0.00005406916,0.00004861117],"genre_scores_gemma":[0.9860725,0.00002352793,0.01296081,0.0001943522,0.00006409514,0.00001285073,0.0003489424,0.00001464459,0.0003082895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2769939,"threshold_uncertainty_score":0.3210358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07359708230822518,"score_gpt":0.3047155145729957,"score_spread":0.2311184322647706,"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."}}