{"meta":{"query_hash":"31c826d13a1b","filters":{"venue":"Frontiers in Imaging"},"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/31c826d13a1b","api":"https://metacan.xera.ac/api/v1/cohort?venue=Frontiers+in+Imaging"},"results":[{"id":"W4394961497","doi":"10.3389/fimag.2024.1373420","title":"ChestBioX-Gen: contextual biomedical report generation from chest X-ray images using BioGPT and co-attention mechanism","year":2024,"lang":"en","type":"article","venue":"Frontiers in Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Alliance de recherche numérique du Canada; New Brunswick Innovation Foundation; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Mechanism (biology); Medicine; Psychology; Philosophy; Epistemology","score_opus":0.014768357407791897,"score_gpt":0.2975488164979846,"score_spread":0.28278045909019267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394961497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035481047,0.0013295384,0.9400947,0.0013251511,0.00025646802,0.00028817359,0.001020045,0.018035512,0.002169391],"genre_scores_gemma":[0.450914,0.0010489464,0.5347824,0.0014092271,0.00027888702,0.00048548996,0.0041677654,0.0007753237,0.0061379117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993088,0.00017337837,0.000042870102,0.0002447474,0.00018600658,0.00004420008],"domain_scores_gemma":[0.9984444,0.0008204547,0.0001646825,0.00021606516,0.00026221093,0.00009214913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015025095,0.0011330956,0.000602204,0.00081637775,0.00022819903,0.0010049193,0.0018459134,0.001231583,0.0020430565],"category_scores_gemma":[0.0044994326,0.00039033545,0.0010285847,0.0004163883,0.0004826555,0.0012671835,0.0015085456,0.001347035,0.00086820644],"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":[0.0007231064,0.00045218226,0.008032408,0.0005922099,0.00023340074,0.0010601308,0.00043596933,0.2279402,0.037922107,0.0055184877,0.02903754,0.68805224],"study_design_scores_gemma":[0.000048237263,0.00012012937,0.0010250829,0.000027202354,0.00005743009,0.0003215166,0.000029356655,0.9765445,0.012732489,0.0035024867,0.0055660103,0.000025625557],"about_ca_topic_score_codex":0.0038949633,"about_ca_topic_score_gemma":0.005476947,"teacher_disagreement_score":0.0038949633,"about_ca_system_score_codex":0.00079194765,"about_ca_system_score_gemma":0.0012031941,"threshold_uncertainty_score":0.007946134},"labels":[],"label_agreement":null},{"id":"W4399868325","doi":"10.3389/fimag.2024.1416114","title":"Intra-video positive pairs in self-supervised learning for ultrasound","year":2024,"lang":"en","type":"article","venue":"Frontiers in Imaging","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasound; Computer science; Artificial intelligence; Psychology; Medicine; Radiology","score_opus":0.011683187555268672,"score_gpt":0.2977274072604365,"score_spread":0.28604421970516786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399868325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07027077,0.0026446236,0.9185837,0.00080965547,0.0002651725,0.00054849713,0.00050991774,0.0037535224,0.002614214],"genre_scores_gemma":[0.6904117,0.00043518824,0.3024921,0.0005656064,0.0002895888,0.00066074746,0.0016079822,0.00034825472,0.0031888508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951551,0.0023385484,0.00022594165,0.0011943936,0.0008776351,0.0002083295],"domain_scores_gemma":[0.990824,0.0056245904,0.00079910265,0.0009311627,0.0015088187,0.00031236836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008579923,0.0014541771,0.0012991825,0.0012563128,0.0006543021,0.0013027056,0.0028134126,0.0019912897,0.00187505],"category_scores_gemma":[0.017350867,0.0005210214,0.00090962136,0.00088756427,0.0013098102,0.0014302139,0.0021029676,0.002319229,0.0008510103],"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":[0.0008590401,0.0006166854,0.006238928,0.0006557355,0.00030779268,0.00018637218,0.00029563726,0.33362216,0.0057352204,0.0060974,0.012620497,0.6327645],"study_design_scores_gemma":[0.000024455212,0.0001763,0.0006305176,0.000034279714,0.000020160789,0.000058245063,0.000037727506,0.9862975,0.00422115,0.007229085,0.00125085,0.000019829247],"about_ca_topic_score_codex":0.0022141584,"about_ca_topic_score_gemma":0.0024777928,"teacher_disagreement_score":0.008579923,"about_ca_system_score_codex":0.0012877126,"about_ca_system_score_gemma":0.0012595124,"threshold_uncertainty_score":0.045375526},"labels":[],"label_agreement":null},{"id":"W4412645318","doi":"10.3389/fimag.2025.1610258","title":"Advances in magnetic particle imaging: evaluating magnetic microspheres and optimized acquisition parameters for high sensitivity cell tracking","year":2025,"lang":"en","type":"article","venue":"Frontiers in Imaging","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Tracking (education); Microsphere; Sensitivity (control systems); Particle (ecology); Magnetic nanoparticles; Magnetic particle imaging; Magnetic particle inspection; Materials science; Nuclear magnetic resonance; Nanotechnology; Physics; Engineering; Chemical engineering; Nanoparticle; Electronic engineering; Biology; Psychology","score_opus":0.004845794875812873,"score_gpt":0.23329297788165237,"score_spread":0.2284471830058395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412645318","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":[0.5424079,0.05851758,0.39146838,0.0009419685,0.00019595926,0.0007676427,0.0006931344,0.0010561425,0.003951331],"genre_scores_gemma":[0.5241057,0.01768838,0.4544828,0.0003144811,0.00012497316,0.0005203373,0.000755081,0.00027398337,0.0017341997],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992213,0.0002437677,0.00007020336,0.00014617536,0.00026359508,0.000054905642],"domain_scores_gemma":[0.99887604,0.00047958796,0.00025384667,0.00006673267,0.00027699096,0.000046749887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033237247,0.0008160543,0.0006389436,0.0005346271,0.00019154415,0.00092092674,0.00058285776,0.0009158026,0.0010163046],"category_scores_gemma":[0.00230974,0.00034923843,0.0003237521,0.0005145415,0.00039508843,0.0010484463,0.000396183,0.0005290847,0.0004384099],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000280801,0.0001365347,0.0018055052,0.00086891797,0.000039685543,0.00006514008,0.000076452205,0.0026440457,0.94526696,0.00086801354,0.00038999232,0.047557935],"study_design_scores_gemma":[0.000053345677,0.0010388138,0.0041558184,0.00008195205,0.00011212707,0.00033832074,0.000039236027,0.015829878,0.9677471,0.00041954906,0.010115544,0.00006832196],"about_ca_topic_score_codex":0.0010617544,"about_ca_topic_score_gemma":0.0011175968,"teacher_disagreement_score":0.0033237247,"about_ca_system_score_codex":0.0007376493,"about_ca_system_score_gemma":0.00088518776,"threshold_uncertainty_score":0.017577708},"labels":[],"label_agreement":null}]}