{"id":"W4388215488","doi":"10.2139/ssrn.4617448","title":"White Blood Cell Microscopic Image Dataset for Segmentation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"White (mutation); Segmentation; Artificial intelligence; Image (mathematics); Computer vision; Computer science; Image segmentation; Pattern recognition (psychology); Biology","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000976967,0.0003812968,0.0003225238,0.0002969933,0.0002213079,0.001721219,0.002280291,0.0001137553,0.000004488213],"category_scores_gemma":[0.00006498555,0.0003927378,0.0002706719,0.000217766,0.00005420938,0.001152912,0.001163214,0.001647733,0.0001218147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005270453,"about_ca_system_score_gemma":0.004285035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003152291,"about_ca_topic_score_gemma":0.00008828222,"domain_scores_codex":[0.9958119,0.00006835815,0.0004956381,0.0007524728,0.0004247215,0.002446958],"domain_scores_gemma":[0.9982498,0.00007300877,0.0004788276,0.0008577037,0.0001637559,0.0001769493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004371891,0.00933171,0.01306316,0.005772463,0.008574918,0.0007557803,0.003060331,0.006447299,0.04410935,0.1405697,0.6575145,0.1103637],"study_design_scores_gemma":[0.00367959,0.0007245966,0.0003985018,0.0002848449,0.0007055867,0.0004581308,0.0004097316,0.003310781,0.01144505,0.973772,0.003370226,0.00144093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02108672,0.004187839,0.9630213,0.002586662,0.002434174,0.001205974,0.004711127,0.0004651341,0.0003011102],"genre_scores_gemma":[0.3671673,0.01513934,0.521743,0.003238115,0.005239702,0.001308415,0.04621444,0.001158506,0.03879119],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8332024,"threshold_uncertainty_score":0.9998525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424670233684267,"score_gpt":0.2798160557242151,"score_spread":0.2655693533873724,"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."}}