{"id":"W2028168305","doi":"10.1177/153303460500400105","title":"Geomatics for Precise 3D Breast Imaging","year":2005,"lang":"en","type":"article","venue":"Technology in Cancer Research & Treatment","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mammography; Imaging phantom; Breast cancer; Medicine; Microcalcification; Artificial intelligence; Breast imaging; Orientation (vector space); Photogrammetry; Nuclear medicine; Computer science; Computer vision; Radiology; Cancer; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002852321,0.0001703488,0.0002947892,0.001269255,0.0001057383,0.00003161171,0.0001640538,0.00009275426,0.00006656686],"category_scores_gemma":[0.00004390499,0.0001365399,0.00009519228,0.0009767427,0.000379077,0.0001442277,0.00006202045,0.0002568224,0.00002874642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117846,"about_ca_system_score_gemma":0.0002494616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002898017,"about_ca_topic_score_gemma":0.0002519308,"domain_scores_codex":[0.9983395,0.0000199716,0.00024181,0.0003713458,0.000279987,0.0007473493],"domain_scores_gemma":[0.9991531,0.0001079967,0.00003500177,0.0004194757,0.0001726525,0.0001118118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002486502,0.0008490983,0.0730302,0.00004823163,0.00009716144,0.00007567836,0.0001542862,0.00001636458,0.00104266,0.0007386463,0.0007466223,0.9229524],"study_design_scores_gemma":[0.06692321,0.01020929,0.129742,0.005945555,0.0009475149,0.009054043,0.006598463,0.02999818,0.1615992,0.05770685,0.5186827,0.002592989],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8955675,0.006165449,0.0002098984,0.08946734,0.0000764222,0.00257155,0.0001588148,0.000406157,0.005376844],"genre_scores_gemma":[0.9917449,0.000626854,0.004867266,0.00007755485,0.0001315263,0.001788433,0.00001736865,0.00003109158,0.0007149624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9203594,"threshold_uncertainty_score":0.5567936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05448889279779843,"score_gpt":0.4266597052865773,"score_spread":0.3721708124887789,"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."}}