{"id":"W4240967627","doi":"10.18535/jmscr/v4i1.05","title":"High Resolution Computed Tomography in Interstitial Lung Diseases","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Science And clinical Research","topic":"Medical Imaging and Pathology Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASTER","funders":"","keywords":"Medicine; Computed tomography; High-resolution computed tomography; Radiology; Resolution (logic); Tomography; Interstitial lung disease; High resolution; Nuclear medicine; Lung; Internal medicine; Artificial intelligence; Remote sensing; Geology","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":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.02277365,0.00006453748,0.0003735542,0.0004775371,0.0001344344,0.00002238052,0.0003607659,0.0001274734,0.0001021363],"category_scores_gemma":[0.04434553,0.00003102941,0.00008740045,0.0008773779,0.006902677,0.0001360668,0.0002581236,0.001005674,0.00000644875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008134323,"about_ca_system_score_gemma":0.001140474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002879505,"about_ca_topic_score_gemma":0.00001088958,"domain_scores_codex":[0.9944693,0.0004196861,0.0007010971,0.0002407389,0.003740292,0.0004288475],"domain_scores_gemma":[0.994361,0.003276033,0.000103275,0.0001394132,0.0008508245,0.001269431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006606741,0.0005620852,0.6279529,0.00003614136,0.0000346077,0.001300956,0.00006764133,2.846979e-8,0.0002106101,0.00059007,0.01663674,0.3519476],"study_design_scores_gemma":[0.004788199,0.002122319,0.9834914,0.002428929,0.00004000382,0.0002855638,0.0002861267,0.0006005324,0.00003786109,0.001893699,0.003949271,0.00007606499],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8921867,0.001435095,0.0009234965,0.1044008,0.0007480574,0.00009322933,0.000001476214,0.00000697206,0.0002041955],"genre_scores_gemma":[0.9945965,0.003273931,0.0002870865,0.0009394647,0.0008637405,0.000001363554,1.841081e-7,0.000002902342,0.00003480037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3555386,"threshold_uncertainty_score":0.9958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.164902793410295,"score_gpt":0.5410084280575058,"score_spread":0.3761056346472108,"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."}}