{"id":"W2098123855","doi":"10.3390/s140814654","title":"Development of a PET Scanner for Simultaneously Imaging Small Animals with MRI and PET","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; TRIUMF; Lawson Health Research Institute; Western University; University of Manitoba; McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Silicon photomultiplier; Positron emission tomography; Scanner; Lyso-; Magnetic resonance imaging; Medical physics; Avalanche photodiode; Preclinical imaging; Physics; Detector; Nuclear medicine; Computer science; Medicine; Optics; Radiology; Scintillator","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001287209,0.0002942914,0.0004640213,0.0004278186,0.0002426149,0.0004668289,0.0009142182,0.0008637136,0.001536997],"category_scores_gemma":[0.0006566737,0.0004715658,0.0003033937,0.0003554115,0.0004892698,0.0006395367,0.0004173613,0.0008578782,0.0006150699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004727841,"about_ca_system_score_gemma":0.001106392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001322357,"about_ca_topic_score_gemma":0.001584443,"domain_scores_codex":[0.9996388,0.00006064225,0.00001376285,0.00006402783,0.0001873681,0.00003554504],"domain_scores_gemma":[0.999656,0.0001025177,0.00002698009,0.0000466184,0.0001174535,0.0000504329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002036219,0.00005397362,0.0007795649,0.0001035851,0.00002023179,0.0001534552,0.00004771817,0.0009496904,0.9639816,0.002328253,0.0006654806,0.03071284],"study_design_scores_gemma":[0.00007202027,0.001186741,0.004355345,0.00003637245,0.00008259835,0.002426403,0.00006328381,0.01447157,0.9169745,0.0005915185,0.05967683,0.00006280271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1744457,0.005491528,0.8001055,0.001504595,0.0003288083,0.001140741,0.0006109255,0.002945827,0.01342639],"genre_scores_gemma":[0.1592495,0.00184753,0.830221,0.0002248907,0.00004524063,0.0003144764,0.0003441086,0.0001178519,0.007635384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001536997,"threshold_uncertainty_score":0.006807506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536147757021099,"score_gpt":0.2776777855393546,"score_spread":0.2623163079691436,"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."}}