{"id":"W4210932317","doi":"10.1007/s11307-022-01705-5","title":"Developing a Microbubble-Based Contrast Agent for Synchrotron Multiple-Image Radiography","year":2022,"lang":"en","type":"article","venue":"Molecular Imaging and Biology","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Light Source (Canada); Saskatoon Medical Imaging; University of Saskatchewan","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Synchrotron; Contrast (vision); Radiography; Medical physics; Radiology; Computer science; Nuclear medicine; Medicine; Computer vision; Optics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00008219579,0.0001135928,0.0001299549,0.0001023548,0.00020274,0.0000230672,0.00009262326,0.0000214943,0.000009120248],"category_scores_gemma":[0.000008648309,0.0001175451,0.00006944421,0.0001124273,0.00005973602,0.00001487983,0.00002218138,0.00008899505,0.000001125171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003691019,"about_ca_system_score_gemma":0.00001770366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001483861,"about_ca_topic_score_gemma":0.000002506433,"domain_scores_codex":[0.9994098,0.00002854326,0.0001204838,0.0001856667,0.00002533901,0.0002301926],"domain_scores_gemma":[0.9997578,0.00006648673,0.00001932355,0.0001086271,0.00001728706,0.00003047251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000697983,0.00001643359,0.002497448,0.00003662228,0.00004457345,0.000003359541,0.00004687089,0.0009440157,0.9896279,0.0006777163,0.0004255559,0.005672516],"study_design_scores_gemma":[0.003830587,0.0001157051,0.002307039,0.00002867879,0.0001147624,0.0001231752,0.0003091019,0.1403896,0.1187953,0.001465467,0.7315794,0.0009411034],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3430121,0.00312912,0.6515616,0.001312947,0.0001322369,0.0004002761,0.0001245862,0.0002174642,0.0001097056],"genre_scores_gemma":[0.9816979,0.00002224299,0.01665336,0.001047327,0.00002148769,0.0003820114,0.0001474762,0.00002511833,0.000003084907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8708326,"threshold_uncertainty_score":0.479335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137727641917906,"score_gpt":0.2321302413245817,"score_spread":0.2207529649054027,"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."}}