{"id":"W2029478600","doi":"10.1088/0031-9155/49/9/012","title":"Functional CT in lung with a conventional scanner: simulations and sampling considerations","year":2004,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Deconvolution; Sampling (signal processing); Scanner; Lung volumes; Noise (video); Mean transit time; Singular value decomposition; Mathematics; Nuclear medicine; Biomedical engineering; Lung; Computer science; Statistics; Medicine; Algorithm; Perfusion scanning; Radiology; Artificial intelligence; Computer vision; Perfusion; Filter (signal processing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005969947,0.0003463748,0.0004016041,0.0003287807,0.0002475102,0.0005446199,0.0007636015,0.001566608,0.001432987],"category_scores_gemma":[0.004180443,0.0002501679,0.0004406575,0.0004609215,0.0004282584,0.0005145285,0.0003229284,0.0004645567,0.000189267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005428321,"about_ca_system_score_gemma":0.0006512679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007705306,"about_ca_topic_score_gemma":0.004316895,"domain_scores_codex":[0.9998119,0.00007958843,0.000009571506,0.00002307843,0.00005896077,0.00001684012],"domain_scores_gemma":[0.9981208,0.001469851,0.0001112278,0.00007962494,0.0001740338,0.00004428411],"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":[0.00008864679,0.00002702901,0.0009483657,0.00007192672,0.00001341213,0.0001609919,0.0000377692,0.988047,0.004388534,0.002143476,0.0002331762,0.003839524],"study_design_scores_gemma":[0.00001159173,0.00002668494,0.000264508,0.000007973787,0.000006722841,0.0000622857,0.000008374326,0.9973943,0.001109312,0.0006991342,0.0004032372,0.000005946084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4528624,0.001205276,0.5317816,0.001151052,0.0001085763,0.0001927782,0.0008337928,0.0006979967,0.01116646],"genre_scores_gemma":[0.9037427,0.0006214095,0.09260587,0.0001550061,0.00002674667,0.0002027408,0.0002970929,0.0001057396,0.00224287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007705306,"threshold_uncertainty_score":0.0153209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.207631760157731,"score_gpt":0.4327344761070327,"score_spread":0.2251027159493016,"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."}}