{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":5,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":5,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"4703f0afe513","filters":{"venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)"}},"results":[{"id":"W4295036131","doi":"10.1109/nss/mic44867.2021.9875845","title":"Deep Active Learning Model for Adaptive PET Attenuation and Scatter Correction in Multi-Centric Studies","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University Hospitals; National Science Foundation","keywords":"Artificial intelligence; Deep learning; Computer science; Transfer of learning; Positron emission tomography; Attenuation; Machine learning; Pattern recognition (psychology); Nuclear medicine; Physics; Medicine","authors":[{"name":"Isaac Shiri","is_ca":false},{"name":"Amirhossein Sanaat","is_ca":false},{"name":"Esmail Jafari","is_ca":false},{"name":"Rezvan Samimi","is_ca":false},{"name":"Maziar Khateri","is_ca":false},{"name":"Peyman Sheikhzadeh","is_ca":false},{"name":"Parham Geramifar","is_ca":false},{"name":"Habibollah Dadgar","is_ca":false},{"name":"Hossein Arabi","is_ca":false},{"name":"Majid Assadi","is_ca":false},{"name":"Carlos Uribe","is_ca":true},{"name":"Arman Rahmim","is_ca":true},{"name":"Habib Zaidi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04610650219420475,"gpt":0.342363961604154,"spread":0.2962574594099492,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001832569,0.001083919,0.001107052,0.0005371996,0.0002922194,0.0007797264,0.002185754,0.001543999,0.001088113],"category_scores_gemma":[0.002904765,0.0005948237,0.001168046,0.0006396857,0.0005383859,0.001152153,0.001127251,0.001953622,0.0003642569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065483,"about_ca_system_score_gemma":0.001252862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00831647,"about_ca_topic_score_gemma":0.008864826,"domain_scores_codex":[0.999494,0.0001457732,0.00003596098,0.0001625492,0.0000860225,0.00007571157],"domain_scores_gemma":[0.9988446,0.0005840109,0.0001075794,0.0001053028,0.0003161876,0.00004232706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002410246,0.0001549759,0.002045654,0.00007308894,0.0001075488,0.0001078912,0.00008971833,0.8729144,0.006585686,0.002980366,0.001695247,0.1130044],"study_design_scores_gemma":[0.000005949056,0.00001809907,0.0001232147,0.000002916343,0.000009770572,0.000008149485,0.000002610745,0.9976816,0.001312612,0.0006448652,0.0001868661,0.000003306545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07960436,0.0005823661,0.9166953,0.0004691441,0.00007692156,0.00007377466,0.0002908415,0.001001202,0.001206211],"genre_scores_gemma":[0.8182616,0.0003634923,0.1738038,0.0003809637,0.00006750486,0.0003289939,0.0008175902,0.0001331253,0.005842959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00831647,"threshold_uncertainty_score":0.01653612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4295036086","doi":"10.1109/nss/mic44867.2021.9875807","title":"Detecting Dopamine Release via PCA of Residuals","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Raclopride; Voxel; Dopamine; False positive paradox; Pattern recognition (psychology); Artificial intelligence; Kernel (algebra); Computer science; Noise (video); Biological system; Mathematics; Dopamine receptor; Neuroscience; Image (mathematics); Biology","authors":[{"name":"Connor Bevington","is_ca":true},{"name":"Jordan Hanania","is_ca":true},{"name":"Ju-Chieh Cheng","is_ca":true},{"name":"Vesna Sossi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01539418382637943,"gpt":0.2956665970091359,"spread":0.2802724131827565,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006134505,0.0007091542,0.0003974277,0.0005918499,0.0001221065,0.0005595813,0.0004533944,0.0004422417,0.0008758469],"category_scores_gemma":[0.003179204,0.0002317185,0.0006140373,0.0005865316,0.000449036,0.0004052132,0.0003823643,0.0006478195,0.0005000712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245663,"about_ca_system_score_gemma":0.0005330353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002407231,"about_ca_topic_score_gemma":0.002380903,"domain_scores_codex":[0.999678,0.00007438687,0.00001611886,0.00008576357,0.0001163373,0.00002945883],"domain_scores_gemma":[0.9992955,0.00031167,0.00009607289,0.0001033064,0.0001713138,0.00002215967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007404627,0.0001877974,0.007048501,0.0003095116,0.0002123537,0.0003733444,0.0002692532,0.3232999,0.2982871,0.01015805,0.002494572,0.3566191],"study_design_scores_gemma":[0.00001709207,0.0001509607,0.007168194,0.00001001802,0.00003982372,0.0002032807,0.0000321561,0.930348,0.05732799,0.003024228,0.001634985,0.00004334374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0919392,0.0001679213,0.9052599,0.0001292707,0.00003485151,0.00004184168,0.0001783124,0.001375079,0.0008736142],"genre_scores_gemma":[0.5950071,0.0004838217,0.4008485,0.00007494891,0.00004043456,0.00006981591,0.0006748774,0.0003310541,0.002469536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002407231,"threshold_uncertainty_score":0.004786491,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297808833","doi":"10.1109/nss/mic44867.2021.9875576","title":"Coupling of 18F-NaF and 18F-FDG PET/CT Dynamic Imaging for the Detection of Arterial Inflammation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"HORIZON EUROPE Health; Health Research","keywords":"Positron emission tomography; Standardized uptake value; Hounsfield scale; Medicine; Calcification; Nuclear medicine; Fluorodeoxyglucose; PET-CT; Artery; Radiology; Computed tomography; Internal medicine","authors":[{"name":"Abdelillah Douhi","is_ca":true},{"name":"Mamdouh S. Al-enezi","is_ca":true},{"name":"Abdelouahed Khalil","is_ca":true},{"name":"Tamàs Fülöp","is_ca":true},{"name":"Éric Turcotte","is_ca":true},{"name":"Michel Nguyen","is_ca":true},{"name":"M’hamed Bentourkia","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.006486318318703598,"gpt":0.2501643486097556,"spread":0.243678030291052,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006968626,0.0003296665,0.0003827835,0.001055803,0.0001805348,0.0004929406,0.0002423749,0.0005063656,0.001043681],"category_scores_gemma":[0.00105317,0.0004034908,0.0002037092,0.0005110028,0.0002371695,0.0002871255,0.0002175316,0.0003030115,0.0002521886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001573665,"about_ca_system_score_gemma":0.0001290188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000764598,"about_ca_topic_score_gemma":0.001358996,"domain_scores_codex":[0.9997271,0.0000931918,0.00001633143,0.00006644632,0.00006270185,0.00003421137],"domain_scores_gemma":[0.9997438,0.0001088088,0.00005110405,0.00002359878,0.00004758213,0.00002518152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003054484,0.0002427096,0.09332357,0.0002565604,0.0001922498,0.001205245,0.0002019254,0.0009464914,0.8180246,0.0001656361,0.0002221349,0.08216447],"study_design_scores_gemma":[0.000204092,0.003457444,0.7622101,0.00009009362,0.0004700046,0.01492679,0.0003505878,0.02423692,0.1888545,0.0005624154,0.004545815,0.00009128889],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702791,0.008290173,0.01954275,0.00006600794,0.00003388172,0.00007336633,0.00009030092,0.00006963003,0.001554792],"genre_scores_gemma":[0.9717779,0.002710414,0.02434741,0.00005485551,0.00004539748,0.0001007212,0.0001281088,0.00002603668,0.0008091682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001055803,"threshold_uncertainty_score":0.003685415,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297786828","doi":"10.1109/nss/mic44867.2021.9875611","title":"A Strategy for Obtaining an Accurate Image Derived Input Function in Dynamic Brain FDG PET","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Voxel; Computer science; Kernel (algebra); Pattern recognition (psychology); Computer vision; Feature (linguistics); Image resolution; Projection (relational algebra); Mathematics; Algorithm","authors":[{"name":"Ju-Chieh Cheng","is_ca":true},{"name":"Connor Bevington","is_ca":true},{"name":"Jordan Hanania","is_ca":true},{"name":"Vesna Sossi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02522902076323103,"gpt":0.3380551577397943,"spread":0.3128261369765633,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008033025,0.0006566829,0.0004876193,0.0008328914,0.0002891813,0.0009126372,0.0006998706,0.0007968564,0.001256914],"category_scores_gemma":[0.002348048,0.000559436,0.0004776387,0.0006712222,0.0003632722,0.0007916609,0.001005414,0.0009993669,0.000967816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000312741,"about_ca_system_score_gemma":0.0005502829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006970377,"about_ca_topic_score_gemma":0.0009756744,"domain_scores_codex":[0.9996912,0.00005582168,0.0000205543,0.00005924126,0.0001369574,0.00003626564],"domain_scores_gemma":[0.9994671,0.000146125,0.00006275343,0.0001162302,0.000180126,0.00002765361],"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":[0.0002973806,0.00007446429,0.001768843,0.0002220923,0.00005078498,0.0004419846,0.0002062945,0.01870697,0.762179,0.007556563,0.0008132327,0.2076824],"study_design_scores_gemma":[0.00002617151,0.0002908187,0.005943296,0.00004161491,0.00006495265,0.002329005,0.00008792889,0.3075479,0.6675958,0.004512811,0.01147194,0.00008778986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01210275,0.0001077844,0.9866991,0.00004861737,0.000008330922,0.00004350558,0.00005150251,0.0004016524,0.0005367025],"genre_scores_gemma":[0.0774048,0.0001584172,0.9211537,0.00004714228,0.0000121573,0.00008953971,0.0001736584,0.0002462517,0.0007143676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001256914,"threshold_uncertainty_score":0.004248321,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4295035338","doi":"10.1109/nss/mic44867.2021.9875540","title":"An Efficient End-to-end Convolutional Neural Network for Classification of Diabetic Retinopathy using ResNet","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Adaptive histogram equalization; Convolutional neural network; Computer science; Artificial intelligence; Diabetic retinopathy; Pattern recognition (psychology); Feature extraction; Fundus (uterus); Confusion matrix; Retinopathy; Computer vision; Histogram; Medicine; Diabetes mellitus; Ophthalmology; Histogram equalization; Image (mathematics)","authors":[{"name":"Faiçal Slimani","is_ca":true},{"name":"M’hamed Bentourkia","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0240486310348164,"gpt":0.307814469353084,"spread":0.2837658383182676,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006069064,0.001199455,0.0006615433,0.0009014149,0.0003494504,0.0004800292,0.001291975,0.0009595361,0.002493694],"category_scores_gemma":[0.0009567487,0.0004433606,0.0007763592,0.0004903103,0.0002304922,0.0006876558,0.0005106756,0.000790393,0.001265876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009787659,"about_ca_system_score_gemma":0.001010643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02247608,"about_ca_topic_score_gemma":0.02192841,"domain_scores_codex":[0.9997316,0.00003313413,0.00001864252,0.00007858229,0.00006483754,0.00007330412],"domain_scores_gemma":[0.999724,0.00007973511,0.00003038703,0.00003345971,0.0001133024,0.0000191119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007589641,0.0006505174,0.00479263,0.0001616134,0.0002656605,0.0003890705,0.00005993485,0.2900162,0.03430869,0.001601981,0.01208221,0.6549126],"study_design_scores_gemma":[0.00001167477,0.00007706857,0.0008731028,0.000008755692,0.00002332699,0.00003959647,0.000006912188,0.9905717,0.007408454,0.0003895058,0.0005791333,0.00001078959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2533589,0.002669627,0.7177688,0.0006077303,0.0003896372,0.0002881131,0.001498027,0.01758801,0.005831179],"genre_scores_gemma":[0.7983754,0.0006394776,0.1868336,0.000392315,0.0001014713,0.0002202115,0.003504571,0.0001758874,0.009757133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02247608,"threshold_uncertainty_score":0.04469049,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}