{"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003579147,0.9959609,0.00072039006,0.00022163044,0.00014036887,0.000006556805,0.000013424162,0.0000143567695,0.0025645057],"genre_scores_gemma":[0.0019649945,0.9958735,0.0007849343,0.00016365554,0.00018679306,0.000009772332,0.000028406947,0.000003005889,0.0009849353],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980444,0.000028077706,0.000022855722,0.00004477042,0.00007997427,0.0000199077],"domain_scores_gemma":[0.9996364,0.0001771508,0.000042546533,0.000011424236,0.00010492785,0.000027462038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005667779,0.00067739526,0.00066701433,0.0024837365,0.000236234,0.0007516949,0.0006065455,0.00074245484,0.004033083],"category_scores_gemma":[0.0006326305,0.0002688986,0.00041261577,0.0022326526,0.00036191355,0.0011879303,0.0006304859,0.0010489039,0.0017676752],"study_design_candidate":"not_applicable","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":[0.0000380577,0.000077714816,0.00021973309,0.0123586375,0.000042944484,0.00019937938,0.0000915095,0.0004939654,0.0050097494,0.0057267314,0.011761825,0.96397954],"study_design_scores_gemma":[0.0000072700363,0.00011835503,0.0007033826,0.0014743527,0.00007993163,0.0014762915,0.000079562786,0.0002636151,0.0024710558,0.0024540236,0.9908476,0.000024585564],"about_ca_topic_score_codex":0.00076441397,"about_ca_topic_score_gemma":0.0009021767,"teacher_disagreement_score":0.004033083,"about_ca_system_score_codex":0.00037041097,"about_ca_system_score_gemma":0.00074969325,"threshold_uncertainty_score":0.013492048},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025918288,0.99081755,0.002242117,0.00038375135,0.0002522395,0.000012626031,0.000027197657,0.000031651303,0.0059737097],"genre_scores_gemma":[0.00243034,0.99206793,0.0019892643,0.0003508159,0.00032271972,0.00001797039,0.00005715664,0.00001120001,0.0027526456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998122,0.000027107415,0.000022427896,0.000039316637,0.00007858503,0.000020449908],"domain_scores_gemma":[0.9995171,0.0002753154,0.000036738275,0.000017735741,0.00012461854,0.000028549706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056592334,0.0007816781,0.0007435282,0.0030181604,0.00028071125,0.0011742058,0.0008639687,0.0013427697,0.0060493136],"category_scores_gemma":[0.0010389586,0.00043774713,0.0004911129,0.0027166978,0.00057831936,0.0023578655,0.0006786935,0.001694408,0.0039272173],"study_design_candidate":"not_applicable","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":[0.00003166436,0.00007109197,0.00021356482,0.007915045,0.00004181821,0.00015256867,0.00007416156,0.00080476364,0.0018742664,0.018013392,0.024491226,0.9463165],"study_design_scores_gemma":[0.000005645581,0.00009430207,0.0004553091,0.0024331831,0.000044584114,0.0013428712,0.000059136728,0.0005826819,0.0012399456,0.0062643173,0.9874512,0.000026934256],"about_ca_topic_score_codex":0.0011640143,"about_ca_topic_score_gemma":0.0016372817,"teacher_disagreement_score":0.0060493136,"about_ca_system_score_codex":0.00066452206,"about_ca_system_score_gemma":0.0008387758,"threshold_uncertainty_score":0.020236969},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9114933,0.00012865628,0.08491104,0.00029421784,0.000027948949,0.00008214794,0.000881394,0.00060048816,0.0015809058],"genre_scores_gemma":[0.98624736,0.000036491114,0.012671432,0.000036740894,0.0000025725426,0.00003130029,0.00041764116,0.00002952467,0.0005269288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999884,0.000032463024,0.0000075097487,0.00002069644,0.000036842437,0.00001850626],"domain_scores_gemma":[0.99925655,0.00046433986,0.00005995609,0.000051792107,0.0001276744,0.00003972334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005219959,0.000519891,0.00034221954,0.00048284992,0.0001726382,0.00043225058,0.00058325235,0.0008751783,0.0012409166],"category_scores_gemma":[0.0017379713,0.0002896288,0.00068213354,0.00032392464,0.00046552802,0.00025172622,0.00036408246,0.00047580013,0.00012429088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008762419,0.000028236716,0.0013938049,0.000021982263,0.000013568632,0.000066229,0.00001734753,0.9941507,0.001868554,0.0002938231,0.00014045705,0.0019176665],"study_design_scores_gemma":[0.000005776849,0.00001742088,0.00039034733,0.0000024522144,0.0000025889335,0.000016376252,0.000004832191,0.99816966,0.0012179656,0.0001239481,0.000045367422,0.0000032267274],"about_ca_topic_score_codex":0.01252401,"about_ca_topic_score_gemma":0.0071015605,"teacher_disagreement_score":0.01252401,"about_ca_system_score_codex":0.0008503011,"about_ca_system_score_gemma":0.0006635711,"threshold_uncertainty_score":0.024902225},"labels":[],"label_agreement":null}]}