{"id":"W4281925782","doi":"10.1002/cyto.a.24404","title":"Volume 101A, Number 6, June 2022 Cover Image","year":2022,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cover (algebra); Cytometry; Computer science; Flow cytometry; Cartography; Biology; Geography; Molecular biology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006763587,0.0001553691,0.0003334831,0.0001819652,0.0002919362,0.00003572604,0.0001823229,0.00004202577,0.04809716],"category_scores_gemma":[0.0003326174,0.0001525515,0.0001538885,0.0008527424,0.0001170269,0.0000791152,0.0002654363,0.000948016,0.002154373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001583348,"about_ca_system_score_gemma":0.00009943806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002018564,"about_ca_topic_score_gemma":1.974963e-7,"domain_scores_codex":[0.9982447,0.00009625525,0.0002692237,0.000349155,0.0006461118,0.0003945737],"domain_scores_gemma":[0.9991356,0.00007180294,0.00008449544,0.0004011533,0.00004765551,0.0002593339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008716487,0.0002343813,0.0931475,0.00005169541,0.00008641677,0.0005046802,0.000130148,0.00003657383,0.005321304,0.0001296348,0.8962027,0.004067806],"study_design_scores_gemma":[0.001319744,0.0001003018,0.01291846,0.00001731768,0.0000758966,0.0005803403,0.000105154,0.005691144,0.0001632062,0.00009218562,0.9787512,0.0001850258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8725485,0.000662428,0.003379866,0.01404333,0.003305031,0.0004646934,0.00005481783,0.0003284418,0.1052129],"genre_scores_gemma":[0.8047686,0.00004532661,0.001930012,0.0047698,0.0007636233,0.00008143361,0.0001527753,0.00007051201,0.1874179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08254854,"threshold_uncertainty_score":0.9986225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009515233084763047,"score_gpt":0.2928195906544873,"score_spread":0.2833043575697242,"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."}}