{"id":"W1880392226","doi":"10.1007/978-3-540-75759-7_109","title":"Tissue Characterization Using Fractal Dimension of High Frequency Ultrasound RF Time Series","year":2007,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractal dimension; High frequency ultrasound; Series (stratigraphy); Fractal; Radio frequency; Ultrasound; Feature (linguistics); Fractal analysis; Materials science; Biomedical engineering; Acoustics; Computer science; Medicine; Mathematics; Physics; Telecommunications; Biology; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009691935,0.0001933841,0.0002910916,0.0003933436,0.0002456585,0.0002200844,0.0009437456,0.00008555347,0.00001957498],"category_scores_gemma":[0.0001333552,0.0001721391,0.00004881645,0.002472312,0.0003071592,0.001607841,0.0003536045,0.0001615276,0.000007957644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009561012,"about_ca_system_score_gemma":0.0001090498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001740845,"about_ca_topic_score_gemma":0.00003148774,"domain_scores_codex":[0.9978837,0.00003420568,0.0004635433,0.0006059463,0.000534621,0.000477958],"domain_scores_gemma":[0.9986547,0.0002159137,0.0002783001,0.0005417286,0.0002165138,0.00009284251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005247255,0.00003714495,0.002112217,0.000009183475,0.000004639463,0.00001731747,0.0007903528,0.009639521,0.7400351,0.0005140086,1.909914e-7,0.2468351],"study_design_scores_gemma":[0.0001358833,0.0001638207,0.0212835,0.00007869244,0.000006492315,0.000138785,0.000001110191,0.3185529,0.6556321,0.003706017,0.00001021463,0.0002904375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4437248,0.0000160025,0.5558073,0.00007635371,0.000270364,0.00006272191,0.000001315876,0.00003433351,0.000006860007],"genre_scores_gemma":[0.639028,0.000001712322,0.3607787,0.00008437286,0.00009564318,3.73558e-7,0.000004319925,0.000005560986,0.000001294215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3089134,"threshold_uncertainty_score":0.701963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008524097129921355,"score_gpt":0.2307602998762426,"score_spread":0.2222362027463212,"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."}}