{"id":"W1601377227","doi":"","title":"APPLICATION OF PATTERN RECOGNITION TECHNIQUES FOR THE ANALYSIS OF THIN BLOOD SMEAR IMAGES","year":2011,"lang":"en","type":"article","venue":"","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Blood smear; Artificial intelligence; Focus (optics); Image processing; Pattern recognition (psychology); Computer vision; Point (geometry); Process (computing); Image (mathematics); Pathology; Medicine; Mathematics; Optics","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":[0.0001546845,0.00005772474,0.0001158038,0.000141744,0.00002100914,0.00002805937,0.0004778615,0.00001561897,0.000009181751],"category_scores_gemma":[0.00003159028,0.00003967198,0.0001344291,0.000453616,0.00005624241,0.0003509305,0.00007899277,0.0000167803,0.000001994276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002529789,"about_ca_system_score_gemma":0.00001109272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002924526,"about_ca_topic_score_gemma":0.00001237026,"domain_scores_codex":[0.9994524,0.00001191145,0.0001875736,0.0001556293,0.0001167615,0.0000757003],"domain_scores_gemma":[0.9991361,0.00009616697,0.0001438703,0.0004221922,0.0001815817,0.00002006652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005535957,0.0004395038,0.007768831,0.00003523927,0.0004842155,2.099238e-7,0.0002780668,0.000003729365,0.001888803,0.002579399,0.0002142601,0.9863022],"study_design_scores_gemma":[0.0001784595,0.0001298306,0.03553681,0.00001715168,0.001510976,0.000001139054,0.00007076479,0.05679941,0.8853962,0.02012316,0.00007710505,0.0001589971],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008210775,0.00004589123,0.9879369,0.00008657701,0.00001098807,0.0002512387,0.00005027173,0.0001114492,0.003295915],"genre_scores_gemma":[0.9213291,0.000005957407,0.07846776,0.00008105094,0.000005275455,0.00007055139,0.00001243522,0.000003809896,0.00002410371],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9861432,"threshold_uncertainty_score":0.1617776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700873492623912,"score_gpt":0.2541885399327367,"score_spread":0.2271798050064976,"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."}}