{"id":"W3033031463","doi":"10.1109/access.2020.2999349","title":"Synthetic Blood Smears Generation Using Locality Sensitive Hashing and Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Locality-sensitive hashing; Locality; Hash function; Artificial intelligence; Hash table; Computer security","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00008381299,0.000137694,0.0001429686,0.00003431064,0.0001348025,0.00164982,0.0004486106,0.0000323633,0.000001047358],"category_scores_gemma":[0.00006930465,0.0001409137,0.00004275461,0.0002836986,0.00007593543,0.002497515,0.0003033165,0.0001006256,0.000002200472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001337207,"about_ca_system_score_gemma":0.00002210511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005849854,"about_ca_topic_score_gemma":0.000008195651,"domain_scores_codex":[0.998885,0.00007113838,0.0001660184,0.0004474117,0.000190249,0.0002401641],"domain_scores_gemma":[0.9993778,0.00005671529,0.00007583036,0.0002228269,0.00007069256,0.0001960911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003338818,0.0003179561,0.0130565,0.0001224688,0.0001881561,0.0007628071,0.001792208,0.7502801,0.01294691,0.001829082,0.0003178957,0.2183525],"study_design_scores_gemma":[0.0001744926,0.00002022269,0.000715572,0.00001194739,0.00003856141,0.00003612734,0.000009719643,0.9893677,0.009320848,0.0001323924,0.000006403488,0.0001660464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.501229,0.0001303651,0.497804,0.0003877859,0.000191906,0.00007647932,0.000002089439,0.0001104351,0.00006794158],"genre_scores_gemma":[0.9950655,0.000002986299,0.00271629,0.00197164,0.0002251456,0.000002313282,0.000002524747,0.00001221072,0.000001351521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4950877,"threshold_uncertainty_score":0.9993865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06005002417230024,"score_gpt":0.2853905798889142,"score_spread":0.225340555716614,"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."}}