{"meta":{"query_hash":"a53e881848c8","filters":{"venue":"Visual Computing for Industry Biomedicine and Art"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a53e881848c8","api":"https://metacan.xera.ac/api/v1/cohort?venue=Visual+Computing+for+Industry+Biomedicine+and+Art"},"results":[{"id":"W3093635528","doi":"10.1186/s42492-020-00061-x","title":"Recent advances in applications of multimodal ultrasound-guided photoacoustic imaging technology","year":2020,"lang":"en","type":"review","venue":"Visual Computing for Industry Biomedicine and Art","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FujiFilm VisualSonics (Canada)","funders":"","keywords":"Photoacoustic imaging in biomedicine; Ultrasound; Ultrasound imaging; Medical imaging; Imaging technology; Biomedical engineering; Photothermal therapy; Doppler imaging; Color doppler; Multi-mode optical fiber; Ultrasonic sensor; Computer science; Materials science; Optics; Radiology; Medicine; Ultrasonography; Nanotechnology; Optical fiber; Physics; Telecommunications","score_opus":0.017393781350809465,"score_gpt":0.33431621285633206,"score_spread":0.3169224315055226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093635528","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000055121538,0.96567786,0.032362483,0.00005448204,0.00025670318,0.0010502944,0.00008600027,0.0002103105,0.00024675534],"genre_scores_gemma":[0.0020922322,0.9956966,0.0014750004,0.000043266708,0.0003575474,0.00009579149,0.00015824614,0.000067871,0.000013449395],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99817204,0.000021384274,0.00084499316,0.0004082668,0.0001397081,0.00041358054],"domain_scores_gemma":[0.9989845,0.0004606292,0.00021300152,0.00016967408,0.000061293584,0.00011087464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023416705,0.00039955456,0.0012424606,0.0006013609,0.000065548826,0.00001665959,0.00022322092,0.00038070465,0.000010832756],"category_scores_gemma":[0.00015740348,0.00036616097,0.000093965566,0.0011978056,0.00021465044,0.00006052086,0.000061595354,0.0009274213,0.000003483612],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015735279,0.000033247277,0.000044103657,0.006341542,0.000047758014,0.000006556637,0.000045390203,0.00012582155,0.00013858653,0.0000118699745,0.00078365236,0.9924199],"study_design_scores_gemma":[0.0004522872,0.00004522917,0.0000021671308,0.0066770115,0.00022575812,0.00019338077,0.00023490345,0.036451746,0.000041359282,0.000036536247,0.95531243,0.0003272128],"about_ca_topic_score_codex":0.0000028287207,"about_ca_topic_score_gemma":5.438859e-7,"teacher_disagreement_score":0.99209267,"about_ca_system_score_codex":0.00009324247,"about_ca_system_score_gemma":0.00011673654,"threshold_uncertainty_score":0.99987906},"labels":[],"label_agreement":null},{"id":"W4383710235","doi":"10.1186/s42492-023-00140-9","title":"Vision transformer architecture and applications in digital health: a tutorial and survey","year":2023,"lang":"en","type":"review","venue":"Visual Computing for Industry Biomedicine and Art","topic":"AI in cancer detection","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Architecture; Computer science; Digital health; Telehealth; Transformer; Artificial intelligence; Digital image processing; Digital image; Segmentation; Telemedicine; Digital imaging; Data science; Health care; Multimedia; Computer vision; Computer architecture; Image processing; Engineering; Image (mathematics); Electrical engineering","score_opus":0.06898679803051441,"score_gpt":0.4118624459068289,"score_spread":0.3428756478763145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383710235","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088752055,0.8489613,0.14525642,0.000554393,0.0010560118,0.002810739,0.00019074074,0.00024978034,0.00003309722],"genre_scores_gemma":[0.0018361827,0.9923433,0.0012816142,0.00019711953,0.0031638474,0.00018136261,0.0006572364,0.000103268365,0.0002360718],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982322,0.00006808731,0.0005532453,0.00064697757,0.00017971738,0.0003198122],"domain_scores_gemma":[0.99868244,0.00073740305,0.00020814192,0.00016622493,0.000034062734,0.00017171432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083448476,0.00029292225,0.00081540627,0.0004388377,0.00020504437,0.0001804576,0.0001863545,0.0004068413,3.2283623e-7],"category_scores_gemma":[0.000056099743,0.00023137564,0.000053005922,0.0009578324,0.00013910144,0.00012952351,0.00014174153,0.00068385585,0.0000013443531],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036478286,0.000018989345,0.00006400357,0.0028380791,0.000019727042,8.937828e-7,0.00013036482,6.64393e-7,2.6565232e-7,0.000053795997,0.00047231038,0.99639726],"study_design_scores_gemma":[0.0005973458,0.00080801017,0.0004252589,0.006046087,0.00003434022,0.000068953,0.000030460533,0.0014341904,4.057925e-7,0.0002836835,0.98995316,0.00031807585],"about_ca_topic_score_codex":0.000056620087,"about_ca_topic_score_gemma":0.000028544653,"teacher_disagreement_score":0.9960792,"about_ca_system_score_codex":0.00005429847,"about_ca_system_score_gemma":0.00020358869,"threshold_uncertainty_score":0.94352245},"labels":[],"label_agreement":null},{"id":"W4399548242","doi":"10.1186/s42492-024-00161-y","title":"Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction","year":2024,"lang":"en","type":"article","venue":"Visual Computing for Industry Biomedicine and Art","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Science Foundation Graduate Research Fellowship Program; National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Artificial intelligence; Computer science; Kernel (algebra); Artificial neural network; Pipeline (software); Projection (relational algebra); Fractal dimension; Pattern recognition (psychology); Fractal; Algorithm; Mathematics","score_opus":0.018135174615359215,"score_gpt":0.30201182961077516,"score_spread":0.2838766549954159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399548242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9625403,0.00014544098,0.035658475,0.0007310176,0.00034309295,0.0004223844,0.000012705198,0.00009244622,0.000054115928],"genre_scores_gemma":[0.99576795,0.000031472697,0.0026836644,0.00013966074,0.0005523222,0.0000028854522,0.0004938191,0.000023987768,0.00030421567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990247,0.0000390932,0.00031593067,0.0002770454,0.00016666863,0.00017655334],"domain_scores_gemma":[0.9995243,0.0000939913,0.000116045405,0.00007540362,0.00009587657,0.0000943882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032725872,0.00014357555,0.00028068852,0.00028303333,0.000090331705,0.00003159443,0.00002677573,0.00009537492,0.000049930728],"category_scores_gemma":[0.00007638336,0.00010460624,0.000040847714,0.00040625254,0.00016476646,0.00010478218,0.000019684565,0.00036815266,0.0000032318576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006887578,0.0002810734,0.012483975,0.0027019407,0.000624159,0.00009735768,0.0005029311,0.00040105538,0.2697939,0.0002411856,0.0012590524,0.7109246],"study_design_scores_gemma":[0.0019893593,0.0018733422,0.015159892,0.002386658,0.00040265985,0.0009291111,0.00012248225,0.96130544,0.006992407,0.000016466063,0.008648036,0.00017415505],"about_ca_topic_score_codex":0.000053991484,"about_ca_topic_score_gemma":7.7998834e-7,"teacher_disagreement_score":0.96090436,"about_ca_system_score_codex":0.000029574823,"about_ca_system_score_gemma":0.00005923548,"threshold_uncertainty_score":0.42657185},"labels":[],"label_agreement":null}]}