{"id":"W299166791","doi":"","title":"Comparative study of shape, intensity and texture features and support vector machine for white blood cell classification","year":2013,"lang":"en","type":"article","venue":"","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Segmentation; Complex wavelet transform; Wavelet; Computer vision; Image segmentation; Wavelet transform; Discrete wavelet transform","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":[0.000052766,0.0001212342,0.0002055015,0.00005997441,0.00005234961,0.0002271892,0.0002147182,0.00002242963,0.00001080726],"category_scores_gemma":[0.0000175337,0.00009329119,0.00002328555,0.0001091636,0.00006565692,0.0006570175,0.0001756653,0.00004984102,0.000003151948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003586336,"about_ca_system_score_gemma":0.00001740753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004952764,"about_ca_topic_score_gemma":0.00002997898,"domain_scores_codex":[0.9992723,0.00001642552,0.0001496148,0.0003137979,0.0001228571,0.0001249751],"domain_scores_gemma":[0.9992961,0.00005930545,0.00008455083,0.0002493162,0.000213522,0.0000972147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000346003,0.01921696,0.732488,0.0007261559,0.0007665404,0.00002069531,0.03001639,0.00002274776,0.03617269,0.02068845,0.1135602,0.04597519],"study_design_scores_gemma":[0.001274246,0.0008802771,0.9710471,0.000007797362,0.00006464162,0.0000161726,0.0008964464,0.02133638,0.003346105,0.0008894628,0.00005549408,0.0001858894],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920018,0.0001537066,0.002206667,0.0004243051,0.0000475738,0.0008979089,0.00002299447,0.00008131895,0.004163714],"genre_scores_gemma":[0.9942535,0.000001648323,0.004737885,0.0001442618,0.000008861284,0.00003339008,0.000009597645,0.00000494979,0.0008058971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2385591,"threshold_uncertainty_score":0.3804304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167981684682331,"score_gpt":0.2604153714826877,"score_spread":0.2387355546358644,"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."}}