{"id":"W4386937850","doi":"10.1007/978-3-031-43950-6_3","title":"Deriving Physiological Information from PET Images Using Machine Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bishop's University; Université TÉLUQ; Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Algorithm; Computer science; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008381036,0.0007859227,0.0005690216,0.001304932,0.00021323,0.00148343,0.000576395,0.001028202,0.003441211],"category_scores_gemma":[0.003896884,0.0003569027,0.0006893615,0.00121897,0.0003701231,0.0009475897,0.0004250616,0.0008121925,0.001385431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004473911,"about_ca_system_score_gemma":0.0004985468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567925,"about_ca_topic_score_gemma":0.001680018,"domain_scores_codex":[0.9997327,0.00008427355,0.00001998371,0.0000821149,0.00005071019,0.00003008964],"domain_scores_gemma":[0.9991879,0.0005171109,0.00008465355,0.00007902642,0.000110065,0.00002117831],"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":[0.0008505597,0.0003555196,0.01540495,0.0007268795,0.0002672077,0.001151978,0.000109325,0.3090535,0.1219042,0.00663338,0.005098835,0.5384437],"study_design_scores_gemma":[0.00002611054,0.0001105229,0.006602655,0.0000456546,0.00005902085,0.0005018737,0.00003769748,0.9428073,0.03822008,0.009227213,0.002318536,0.00004335173],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08672974,0.001558856,0.9031145,0.0005527431,0.0001614839,0.0001543398,0.001319074,0.002235563,0.004173812],"genre_scores_gemma":[0.7415048,0.001404435,0.2520196,0.0003597968,0.0001907363,0.000148844,0.001494809,0.0002223743,0.002654581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003441211,"threshold_uncertainty_score":0.01151198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618224470361048,"score_gpt":0.3042825943059599,"score_spread":0.2681003496023495,"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."}}