{"id":"W3081030069","doi":"10.1142/s0219467821500108","title":"Classification of Mammogram Abnormalities Using Legendre Moments","year":2020,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fowler Kennedy Sport Medicine Clinic; Western University","funders":"","keywords":"Extractor; Legendre polynomials; Feature extraction; Classifier (UML); Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Computer science; Mathematics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002122382,0.00006971598,0.0001221889,0.0001742728,0.0000300634,0.0001109005,0.0005458717,0.00003542105,0.000005857709],"category_scores_gemma":[0.00006496003,0.00006050554,0.00008498097,0.0002129292,0.0000893151,0.0008189229,0.00008216154,0.0001214002,6.454574e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001434406,"about_ca_system_score_gemma":0.0000559776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006278986,"about_ca_topic_score_gemma":2.245423e-7,"domain_scores_codex":[0.9989737,0.00003599934,0.000398846,0.00009500401,0.0004261992,0.00007026848],"domain_scores_gemma":[0.9985718,0.00003481487,0.0004402385,0.00008171066,0.0008020508,0.00006945839],"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":[0.0002364199,0.0003801213,0.02353664,0.0001475391,0.000543886,0.0001737433,0.004266285,0.000009637329,0.5196564,0.3538406,0.001416627,0.09579209],"study_design_scores_gemma":[0.003293426,0.00138234,0.1127372,0.0004812897,0.0001537142,0.001387842,0.002051066,0.1380151,0.6681978,0.04722286,0.02412114,0.0009562243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08937742,0.0003549849,0.9046664,0.005064893,0.0002077295,0.00005169548,0.00001002667,0.00002386024,0.0002429894],"genre_scores_gemma":[0.9719532,0.000457031,0.02710349,0.0003682953,0.00009970604,5.198322e-7,0.000001623617,0.00000409933,0.00001210025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8825757,"threshold_uncertainty_score":0.2467344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04327668158494356,"score_gpt":0.3047382274338556,"score_spread":0.261461545848912,"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."}}