{"id":"W2057729245","doi":"10.1118/1.3537289","title":"Frame‐to‐frame image realignment assessment tool for dynamic brain positron emission tomography","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Sport Centre Pacific","funders":"","keywords":"Computer vision; Artificial intelligence; Image quality; Computer science; Frame (networking); Metric (unit); Positron emission tomography; Nuclear medicine; Image (mathematics); Engineering; Medicine","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":[0.003864089,0.0009325253,0.0006056051,0.002257834,0.0003126596,0.001018836,0.001225589,0.0006793354,0.002669022],"category_scores_gemma":[0.01492462,0.0003852724,0.0004704014,0.0008395787,0.000510336,0.0009065571,0.0007307443,0.0006422242,0.0008678118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006153909,"about_ca_system_score_gemma":0.0005751744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001245536,"about_ca_topic_score_gemma":0.00147417,"domain_scores_codex":[0.9978353,0.0005914829,0.0001658566,0.0002718332,0.001061758,0.00007376657],"domain_scores_gemma":[0.9941574,0.002218303,0.001029487,0.000579413,0.0018857,0.0001296247],"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":[0.001518115,0.000275246,0.0105823,0.0004566206,0.0001700397,0.0002079357,0.0002650372,0.01341969,0.1571907,0.001892075,0.002839983,0.8111823],"study_design_scores_gemma":[0.0002130487,0.003208681,0.0648222,0.0002073992,0.0003026706,0.002760118,0.0001903808,0.6353431,0.2753635,0.002768877,0.01450215,0.0003177371],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06202681,0.0005706326,0.9317356,0.00007005635,0.00004741213,0.000318963,0.0002137804,0.004274319,0.0007422703],"genre_scores_gemma":[0.2583156,0.0002113673,0.7392964,0.00004783244,0.0000337042,0.0005018798,0.0004886895,0.0002947485,0.0008099036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003864089,"threshold_uncertainty_score":0.02043551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172722955190896,"score_gpt":0.3523034238539626,"score_spread":0.335031128334873,"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."}}