{"id":"W4367848984","doi":"10.32920/22734392","title":"Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Complex wavelet transform; Curvelet; Feature extraction; Discriminative model; Wavelet; Discrete wavelet transform; Wavelet transform","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003541915,0.0003551065,0.0003134377,0.001278111,0.0000977208,0.0004483515,0.0002382299,0.0003650257,0.0005623368],"category_scores_gemma":[0.0008686288,0.0001369586,0.0005396761,0.0005866418,0.0001196581,0.0003604026,0.0003088591,0.0003086328,0.0003842432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001363059,"about_ca_system_score_gemma":0.0001973997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008602713,"about_ca_topic_score_gemma":0.0006853787,"domain_scores_codex":[0.9998486,0.00002040305,0.00001367183,0.00002814272,0.00007060722,0.0000185772],"domain_scores_gemma":[0.9998117,0.00004341821,0.00004043743,0.0000146474,0.00007205769,0.00001773589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004152939,0.0001362082,0.02628548,0.0002356725,0.0000985273,0.001049163,0.0001763684,0.0185283,0.1753544,0.001282108,0.002392101,0.7740464],"study_design_scores_gemma":[0.00002916479,0.0003504023,0.06285926,0.00006045749,0.000116465,0.003144129,0.0002769116,0.8753024,0.05210902,0.001616306,0.004086391,0.00004897654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3680928,0.001586009,0.6268307,0.0003205547,0.0001202486,0.0001125694,0.0004182006,0.0006107221,0.001908231],"genre_scores_gemma":[0.7934697,0.001576417,0.2023231,0.00006910608,0.00009061637,0.00008230125,0.0006053771,0.00003726813,0.001746011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001278111,"threshold_uncertainty_score":0.001881242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2240200482776367,"score_gpt":0.4257833825364064,"score_spread":0.2017633342587696,"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."}}