{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":16,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":16,"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":"9eb80d156e8b","filters":{"venue":"IEEE Open Journal of Engineering in Medicine and Biology"}},"results":[{"id":"W3027360693","doi":"10.1109/ojemb.2021.3053215","title":"Face Coverings, Aerosol Dispersion and Mitigation of Virus Transmission Risk","year":2021,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Infection Control and Ventilation","field":"Medicine","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Medical Research Council; Directorate for Biological Sciences; Higher Education Commision, Pakistan; FP7 Ideas: European Research Council; Hospital for Sick Children; Higher Education Commission, Pakistan","keywords":"Face masks; Aerosol; Schlieren; Transmission (telecommunications); Flow (mathematics); Jet (fluid); Coronavirus disease 2019 (COVID-19); Leakage (economics)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02105596632881231,"gpt":0.2994644960801363,"spread":0.278408529751324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003512884,0.0003375117,0.0002425046,0.0003570019,0.0002953201,0.0005443253,0.0002522704,0.0004913819,0.001400854],"category_scores_gemma":[0.0007573857,0.0001089353,0.0002649121,0.00009341687,0.0002923217,0.0003907988,0.0003399597,0.0003169254,0.0002040957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002426609,"about_ca_system_score_gemma":0.0001978527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007369895,"about_ca_topic_score_gemma":0.000856644,"domain_scores_codex":[0.9997213,0.00006045813,0.00001182553,0.00003585686,0.0001320329,0.00003846946],"domain_scores_gemma":[0.9996247,0.0001229294,0.0001235059,0.00002505861,0.00007466435,0.00002914583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006345949,0.000136103,0.01953244,0.0005303564,0.00006987094,0.0003494893,0.0002382408,0.002123346,0.9101695,0.0004311828,0.0003922809,0.06539257],"study_design_scores_gemma":[0.00003413281,0.00610455,0.1339089,0.0002356287,0.0002744761,0.002396537,0.0009325871,0.008455174,0.8356367,0.0006413769,0.01132115,0.00005881439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868722,0.004794774,0.005348564,0.0002051437,0.0000833513,0.00005498461,0.00006241611,0.0000580668,0.002520495],"genre_scores_gemma":[0.9929689,0.0010528,0.004689093,0.00009647035,0.00003303391,0.00001697331,0.00004569788,0.00001078793,0.001086159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001400854,"threshold_uncertainty_score":0.004686356,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205956456","doi":"10.1109/ojemb.2022.3143686","title":"Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"York University; National Science Foundation","keywords":"Stability (learning theory); Control (management); Controller (irrigation); Layer (electronics); Supervised learning; Cognition; Robustness (evolution)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.06370519291937121,"gpt":0.3574327304973653,"spread":0.2937275375779941,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004519779,0.0004980196,0.0002695326,0.0001700865,0.0002565393,0.0005904473,0.0008110428,0.0004348329,0.001480376],"category_scores_gemma":[0.001287857,0.0001849169,0.0003708935,0.0000995458,0.0004332433,0.0005398749,0.0006327044,0.000515999,0.0002811404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119608,"about_ca_system_score_gemma":0.0005246316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001565973,"about_ca_topic_score_gemma":0.001951793,"domain_scores_codex":[0.9998087,0.00003637118,0.00001319047,0.00006948255,0.00004442288,0.00002775788],"domain_scores_gemma":[0.9995055,0.0001442536,0.00009997789,0.00007792979,0.0001440352,0.00002831991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004438039,0.0007953066,0.00461523,0.0003872623,0.0002104664,0.0001827391,0.0004023528,0.5254815,0.2099257,0.01031588,0.002396787,0.2448431],"study_design_scores_gemma":[0.00003104597,0.0002472896,0.001412717,0.00001477285,0.0000271748,0.00002883029,0.0000168899,0.9792055,0.01560059,0.002236355,0.001159972,0.00001887625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.110419,0.0002373216,0.8823975,0.0002389456,0.0001079333,0.0001290366,0.00005158359,0.001236053,0.005182686],"genre_scores_gemma":[0.9564481,0.00006161667,0.04220388,0.00007906232,0.00002614075,0.0001180409,0.00003222228,0.00002134002,0.001009645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001565973,"threshold_uncertainty_score":0.004952312,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402260201","doi":"10.1109/ojemb.2024.3453049","title":"Ocular Biomechanical Responses to Long-Duration Spaceflight","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Spaceflight effects on biology","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Hôpital Maisonneuve-Rosemont","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Canadian Space Agency; National Aeronautics and Space Administration","keywords":"Spaceflight; Duration (music); Physical medicine and rehabilitation; Biomechanics; Medicine; Aeronautics; Engineering; Physiology; Physics; Aerospace engineering","authors":[{"name":"Marissé Masís Solano","is_ca":true},{"name":"R. Dumas","is_ca":true},{"name":"Mark R. Lesk","is_ca":true},{"name":"Santiago Costantino","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04726053219686399,"gpt":0.3752637172382212,"spread":0.3280031850413572,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002181328,0.000246158,0.0001462429,0.0002599868,0.0001827412,0.0002103533,0.00009268282,0.0002462462,0.002301868],"category_scores_gemma":[0.0007391893,0.00009914782,0.0001980319,0.0001580511,0.0002015715,0.0001207563,0.0002786891,0.0003140392,0.0002006637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002515276,"about_ca_system_score_gemma":0.0001790112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499568,"about_ca_topic_score_gemma":0.003428276,"domain_scores_codex":[0.9999034,0.00002060385,0.000007526465,0.00001937902,0.00002806675,0.00002101772],"domain_scores_gemma":[0.9996107,0.00007556834,0.0001310807,0.00003437,0.00006733557,0.00008095756],"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":[0.01123099,0.000868173,0.373943,0.0002729726,0.000392724,0.001362178,0.001148914,0.001194539,0.552125,0.00007415707,0.001281319,0.05610598],"study_design_scores_gemma":[0.00001459944,0.002158804,0.9907504,0.00000950892,0.0000214613,0.0003580746,0.0002980913,0.0002372044,0.005888905,0.00001486991,0.0002415295,0.00000657904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994098,0.0001134091,0.00009354248,0.00001592145,0.000005237231,0.000006571246,0.0001314129,0.000004092471,0.0002200754],"genre_scores_gemma":[0.9989712,0.00009533912,0.00008875928,0.00004028148,0.000008878895,0.00001887422,0.0002869327,0.000002320395,0.0004872932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002499568,"threshold_uncertainty_score":0.007700503,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4295832405","doi":"10.1109/ojemb.2022.3202435","title":"Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Innovation, Science and Economic Development Canada; Élisabeth Bruyère Hospital; National Research Council Canada; Carleton University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Spectrogram; Artificial intelligence; Computer science; Wavelet; Classifier (UML); Dry cough; Transfer of learning; Pattern recognition (psychology); Speech recognition; Medicine; Internal medicine","authors":[{"name":"Madison Cohen-McFarlane","is_ca":true},{"name":"Pengcheng Xi","is_ca":true},{"name":"Bruce Wallace","is_ca":true},{"name":"Karim Habashy","is_ca":true},{"name":"Saiful Huq","is_ca":true},{"name":"Rafik Goubran","is_ca":true},{"name":"Frank Knoefel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3453911005191784,"gpt":0.429778363096697,"spread":0.0843872625775186,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002916511,0.001152117,0.0006643799,0.001760037,0.0002191547,0.001275805,0.001001324,0.001402341,0.002957486],"category_scores_gemma":[0.007140723,0.0001760495,0.0007719495,0.0007520287,0.0003278943,0.0008353418,0.000802657,0.0006843269,0.000902074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008414276,"about_ca_system_score_gemma":0.0005594312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00665522,"about_ca_topic_score_gemma":0.005683347,"domain_scores_codex":[0.9986793,0.0003864382,0.0001022705,0.00026579,0.0004721103,0.00009399149],"domain_scores_gemma":[0.9980555,0.0008831261,0.000124287,0.0001614765,0.0006842993,0.00009130204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002427872,0.001261373,0.01644629,0.00079884,0.0005312069,0.0002407248,0.0001619398,0.1406261,0.07870685,0.0008238211,0.004767317,0.7532077],"study_design_scores_gemma":[0.00008488334,0.0008398256,0.01715068,0.00004263439,0.0001140575,0.0001474668,0.0001169098,0.927992,0.0519551,0.0005230996,0.0009997244,0.00003355084],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7039849,0.002203806,0.2746074,0.00106265,0.0004529849,0.0008128367,0.002613226,0.007497353,0.006764902],"genre_scores_gemma":[0.8968872,0.0004137268,0.09734708,0.0001731276,0.00007696557,0.0002118501,0.00236915,0.0001413597,0.002379606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00665522,"threshold_uncertainty_score":0.01542419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362011113","doi":"10.1109/ojemb.2023.3262965","title":"Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Artificial intelligence; Computer science; Inference; Machine learning; Consistency (knowledge bases)","authors":[{"name":"Enamundram Naga Karthik","is_ca":true},{"name":"Farida Chériet","is_ca":true},{"name":"Catherine Laporte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06183974268112979,"gpt":0.3749658666873268,"spread":0.313126124006197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001425318,0.0006271295,0.0005945241,0.0006215427,0.0003224135,0.0009212479,0.0009911422,0.0009913901,0.0008020756],"category_scores_gemma":[0.00830128,0.0006524396,0.0006322752,0.0003905664,0.001012333,0.001233887,0.00165582,0.001237593,0.0001892995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007825915,"about_ca_system_score_gemma":0.00105696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003053748,"about_ca_topic_score_gemma":0.0046678,"domain_scores_codex":[0.9994159,0.0001350386,0.00003403471,0.0001500836,0.0002216476,0.00004335424],"domain_scores_gemma":[0.9977949,0.001393944,0.0002958374,0.0001915566,0.000265648,0.0000582331],"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":[0.0001483705,0.00001692969,0.001135779,0.0001066724,0.00004641719,0.0001099494,0.0001516939,0.9018906,0.01196027,0.00955007,0.0005699074,0.07431332],"study_design_scores_gemma":[0.000005054215,0.00001705095,0.0002780053,0.00001061071,0.000006317637,0.00004488242,0.000008950182,0.9882072,0.004103587,0.006896376,0.0004117755,0.0000102077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02018779,0.0001342264,0.9786018,0.0001285132,0.00001024423,0.00002207744,0.00005795717,0.0003219786,0.0005354361],"genre_scores_gemma":[0.7271849,0.0001920468,0.2705819,0.0001818438,0.00003898205,0.0001017604,0.0002955555,0.0003012521,0.001121756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003053748,"threshold_uncertainty_score":0.007537842,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4396941433","doi":"10.1109/ojemb.2024.3401571","title":"NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"National Health and Medical Research Council; Medical Research Future Fund; National Stem Cell Foundation of Australia; Monash University","keywords":"Security token; Computer science; Speech recognition; Deep learning; Convolution (computer science); Artificial intelligence; Auscultation; Generator (circuit theory); Pattern recognition (psychology); Artificial neural network; Medicine","authors":[{"name":"Yang Yi Poh","is_ca":false},{"name":"Ethan Grooby","is_ca":true},{"name":"Kenneth Tan","is_ca":false},{"name":"Lindsay Zhou","is_ca":false},{"name":"Arrabella King","is_ca":false},{"name":"Ashwin Ramanathan","is_ca":false},{"name":"Anil K. Malhotra","is_ca":false},{"name":"Mehrtash Harandi","is_ca":false},{"name":"Faezeh Marzbanrad","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0400703600832602,"gpt":0.3640559218640544,"spread":0.3239855617807942,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004532153,0.001071182,0.0004921966,0.0005372879,0.0001841833,0.0004222931,0.001356983,0.0007633778,0.004935294],"category_scores_gemma":[0.0009027644,0.0003455547,0.0004796857,0.0003476552,0.0001723976,0.0007174874,0.0009261466,0.0007804021,0.001674103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007287279,"about_ca_system_score_gemma":0.001307786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007552675,"about_ca_topic_score_gemma":0.01250007,"domain_scores_codex":[0.9998415,0.00002195262,0.00001064266,0.00004987223,0.00004866748,0.00002741413],"domain_scores_gemma":[0.9998141,0.00006527118,0.00001548438,0.00002066833,0.00006178177,0.00002281479],"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":[0.000943528,0.0003868402,0.004635214,0.0002250788,0.0001883874,0.0003505765,0.00004812648,0.1509736,0.02081291,0.002371516,0.03886726,0.780197],"study_design_scores_gemma":[0.00003891998,0.000081064,0.0006671788,0.00001270919,0.00001925622,0.00007897469,0.00001116949,0.9850416,0.01072729,0.0009844436,0.002322594,0.00001489085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08004794,0.001225551,0.8636743,0.0006845886,0.0006455534,0.0002622701,0.003870444,0.04307867,0.006510731],"genre_scores_gemma":[0.52809,0.0006567144,0.4390006,0.0007682952,0.0001440026,0.0005118679,0.0129301,0.0007290529,0.01716937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007552675,"threshold_uncertainty_score":0.01651019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3101722601","doi":"10.1109/ojemb.2020.3036742","title":"The COSMIC Bubble Helmet: A Non-Invasive Positive Pressure Ventilation System for COVID-19","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Abbotsford Veterinary Clinic; Fraser Health; BC Research (Canada); Vancouver General Hospital; University of British Columbia; Surrey Memorial Hospital; University of Victoria; McGill University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Aerosolization; Acute respiratory distress; Cabin pressurization; Personal protective equipment; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Mechanical ventilation; COSMIC cancer database; Ventilation (architecture); Continuous positive airway pressure; Materials science; Medicine; Anesthesia; Mechanical engineering; Engineering; Lung; Physics; Composite material; Inhalation; Internal medicine; Astrophysics","authors":[{"name":"Vionarica Gusti","is_ca":true},{"name":"Wan Wu","is_ca":true},{"name":"Arpan Grover","is_ca":true},{"name":"Sabian Chiu","is_ca":true},{"name":"Kai-Wen Su","is_ca":false},{"name":"Erica Ma","is_ca":true},{"name":"Chanelle Chow","is_ca":true},{"name":"Ella Sit","is_ca":true},{"name":"Jun Hyeok Lim","is_ca":true},{"name":"Abhijit Pandhari","is_ca":true},{"name":"Mattias Park","is_ca":true},{"name":"Ryan Lee","is_ca":true},{"name":"Faisal Shahril","is_ca":true},{"name":"Shawn T. Lim","is_ca":false},{"name":"Christopher Nguan","is_ca":true},{"name":"Dan Driedger","is_ca":true},{"name":"Avinash Sinha","is_ca":true},{"name":"Ivan G. Scrooby","is_ca":true},{"name":"Neilson McLean","is_ca":true},{"name":"Michael W. Lee","is_ca":true},{"name":"Tyler D. Yan","is_ca":true},{"name":"The COSMIC Team","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06511546014781723,"gpt":0.3415325691690158,"spread":0.2764171090211986,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001111561,0.0005453346,0.0003765559,0.0004329444,0.0005324198,0.001001313,0.001361746,0.001209026,0.02279602],"category_scores_gemma":[0.001153888,0.0002146746,0.0002717978,0.0002470872,0.0002042298,0.0009641856,0.001018997,0.0006025829,0.009621572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006495982,"about_ca_system_score_gemma":0.001070681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004116782,"about_ca_topic_score_gemma":0.006274845,"domain_scores_codex":[0.9991987,0.00008841803,0.00007021154,0.00009952907,0.0004705244,0.00007264203],"domain_scores_gemma":[0.9996328,0.00004602567,0.00002735668,0.00003301668,0.0001727817,0.00008789454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001421047,0.0005783222,0.007778611,0.0008638355,0.00005605954,0.0008990272,0.0004089522,0.001153807,0.184486,0.005476722,0.4171908,0.3796867],"study_design_scores_gemma":[0.0006483998,0.002029081,0.02234661,0.000306395,0.0001278989,0.00413827,0.0002200157,0.02992715,0.1183997,0.001212525,0.8203616,0.0002822985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1374751,0.004434679,0.4528689,0.009413772,0.002793433,0.006816804,0.01330864,0.1129474,0.2599413],"genre_scores_gemma":[0.4273043,0.003056472,0.2755954,0.006580569,0.001097121,0.003342166,0.03601936,0.005686433,0.2413181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02279602,"threshold_uncertainty_score":0.07626033,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392931885","doi":"10.1109/ojemb.2024.3377923","title":"Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"York University; Directorate for Computer and Information Science and Engineering; New York University; National Science Foundation","keywords":"Inference; Bayesian inference; Arousal; Cognitive psychology; Cognition; Bayesian probability; Psychology; Computer science; Artificial intelligence; Speech recognition; Social psychology; Neuroscience","authors":[{"name":"Saman Khazaei","is_ca":false},{"name":"Md. Rafiul Amin","is_ca":false},{"name":"Rose T. Faghih","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04736944648018551,"gpt":0.3129244589469248,"spread":0.2655550124667393,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001615996,0.0007057038,0.000684189,0.0004341725,0.0002502027,0.0009740397,0.001285883,0.001212049,0.001964619],"category_scores_gemma":[0.008200869,0.0007167766,0.0007883133,0.0003601759,0.0007545365,0.001104204,0.0007584167,0.001556595,0.0005396874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007945035,"about_ca_system_score_gemma":0.001105667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008081119,"about_ca_topic_score_gemma":0.009428702,"domain_scores_codex":[0.9995907,0.0001219136,0.00002023026,0.0001462083,0.00005918922,0.00006171421],"domain_scores_gemma":[0.9980382,0.001502072,0.0001571718,0.0001007504,0.0001200836,0.00008177053],"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":[0.0005284472,0.0002454997,0.01136593,0.0001828717,0.0001961104,0.0001634016,0.0003091273,0.8266338,0.01417193,0.03909512,0.002578497,0.1045294],"study_design_scores_gemma":[0.00001373279,0.00002085011,0.002119036,0.00000966674,0.00001277358,0.00001567245,0.000007052173,0.9887988,0.001147311,0.00765668,0.0001808576,0.00001761323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1112207,0.0001749825,0.884682,0.0007825094,0.00004347882,0.00005428714,0.0004457813,0.0004077224,0.002188518],"genre_scores_gemma":[0.8972162,0.0002484893,0.09616388,0.0002090918,0.0000761376,0.0001224442,0.001084782,0.0001005204,0.004778369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008081119,"threshold_uncertainty_score":0.01606816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388740177","doi":"10.1109/ojemb.2023.3332839","title":"Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"York University; Directorate for Computer and Information Science and Engineering; New York University; National Science Foundation","keywords":"Computer science; Noise (video); Sudomotor; Pattern recognition (psychology); Artificial intelligence; Algorithm","authors":[{"name":"Samiul Alam","is_ca":false},{"name":"Md. Rafiul Amin","is_ca":false},{"name":"Rose T. Faghih","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07863482283351873,"gpt":0.3508746161567393,"spread":0.2722397933232205,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008200872,0.0006280261,0.0004496317,0.0003638312,0.0001752254,0.0005270495,0.0006960658,0.0008513769,0.002171882],"category_scores_gemma":[0.003394549,0.0003687488,0.000849461,0.0006173106,0.00037855,0.0006547556,0.0006618652,0.001120347,0.0007111782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003472751,"about_ca_system_score_gemma":0.000787377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004493381,"about_ca_topic_score_gemma":0.006082948,"domain_scores_codex":[0.9996798,0.0001084946,0.00001853395,0.00007841802,0.00008576683,0.00002890671],"domain_scores_gemma":[0.9991885,0.0004489327,0.00008318387,0.0001276546,0.000126859,0.00002498292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001288794,0.0000949872,0.001180183,0.0001567195,0.00008806538,0.0001200148,0.00009607737,0.7728447,0.02106679,0.01084564,0.003894375,0.1894836],"study_design_scores_gemma":[0.000003361581,0.00001312554,0.0004884152,0.000005611964,0.000004016412,0.00002064634,0.000003643694,0.9956245,0.001796752,0.001373966,0.0006588979,0.000007139886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008917303,0.00009163354,0.9897628,0.0001242236,0.00002699973,0.0000263899,0.0002066006,0.0003877927,0.0004562718],"genre_scores_gemma":[0.3276635,0.0005730186,0.6628172,0.0002429887,0.0001467884,0.0003069228,0.002227525,0.0002362152,0.005785882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004493381,"threshold_uncertainty_score":0.008934438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313598855","doi":"10.1109/ojemb.2022.3233778","title":"Modeling and Prediction of a Guidewire's Reachable Workspace and Deliverable Forces","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Deliverable; Workspace; Computer science; Imaging phantom; Finite element method; Simulation; Biomedical engineering; Robot; Artificial intelligence; Radiology; Engineering; Medicine; Structural engineering; Systems engineering","authors":[{"name":"Afsoon Nejati Aghdam","is_ca":true},{"name":"M. Ali Tavallaei","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04693892878873943,"gpt":0.2980054888138332,"spread":0.2510665600250938,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004796807,0.0006217415,0.0003121709,0.0005432322,0.0002539512,0.0006804396,0.000653462,0.001340789,0.0009556891],"category_scores_gemma":[0.001957194,0.0004488604,0.0004735333,0.0001832275,0.000517805,0.0005051122,0.000358113,0.0003316139,0.000320127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006085218,"about_ca_system_score_gemma":0.0009910209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007130956,"about_ca_topic_score_gemma":0.003935358,"domain_scores_codex":[0.9998283,0.00003598911,0.0000102022,0.00003236937,0.00006864782,0.00002438417],"domain_scores_gemma":[0.9994376,0.000356133,0.00007453392,0.00003878295,0.00006772781,0.00002515073],"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":[0.00002684612,0.00001913228,0.0007420738,0.0000169312,0.000003996768,0.0000687127,0.00003892956,0.9911857,0.00398696,0.0007138054,0.00006697889,0.003129817],"study_design_scores_gemma":[0.000003337534,0.00001534698,0.0001869119,0.000002394878,0.000002387695,0.00001619851,0.000005088423,0.9982362,0.001271744,0.0001330179,0.0001241449,0.000003247234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3414709,0.0002465108,0.6490613,0.0002004519,0.00002559957,0.000104404,0.0001919649,0.0009729606,0.007725823],"genre_scores_gemma":[0.9394071,0.0002003367,0.05711034,0.00001778333,0.000005650968,0.0001240173,0.0001033253,0.00006318842,0.002968419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007130956,"threshold_uncertainty_score":0.01417887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407097705","doi":"10.1109/ojemb.2025.3537760","title":"BandFocusNet: A Lightweight Model for Motor Imagery Classification of a Supernumerary Thumb in Virtual Reality","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Tamkeen; York University; New York University Abu Dhabi","keywords":"Supernumerary; Thumb; Motor imagery; Virtual reality; Computer science; Artificial intelligence; Psychology; Physical medicine and rehabilitation; Medicine; Neuroscience; Brain–computer interface; Anatomy","authors":[{"name":"Haneen Alsuradi","is_ca":false},{"name":"Joseph Hong","is_ca":false},{"name":"Alireza Sarmadi","is_ca":false},{"name":"Robert Volcic","is_ca":false},{"name":"Hanan Salam","is_ca":false},{"name":"S. Farokh Atashzar","is_ca":false},{"name":"Farshad Khorrami","is_ca":false},{"name":"Mohamad Eid","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07825421465522349,"gpt":0.3463154326274567,"spread":0.2680612179722333,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004805205,0.0009244045,0.0006195082,0.0004534186,0.0002382898,0.0004893976,0.001420799,0.0009377251,0.002069298],"category_scores_gemma":[0.0008850393,0.000298227,0.0006104122,0.0002635779,0.0002343002,0.0005137292,0.0005470237,0.0008223436,0.0006464167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006679434,"about_ca_system_score_gemma":0.0006848038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01353153,"about_ca_topic_score_gemma":0.01521241,"domain_scores_codex":[0.9998653,0.00002566911,0.000006658425,0.00004640524,0.00002375121,0.0000322703],"domain_scores_gemma":[0.9998203,0.00007204806,0.00001964183,0.00002395562,0.00004310671,0.00002093907],"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":[0.0007476486,0.0004305344,0.004785622,0.0001378003,0.0001643342,0.0002072351,0.0001023959,0.3624372,0.02260643,0.001514177,0.007121336,0.5997454],"study_design_scores_gemma":[0.000006167595,0.0000631904,0.0005202986,0.000005973393,0.000008545246,0.00002115334,0.000009360831,0.9967144,0.001930739,0.0003738182,0.000341435,0.000004979385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3104818,0.001282409,0.6753985,0.0005325116,0.0002546133,0.0002131163,0.0008146522,0.007727009,0.003295312],"genre_scores_gemma":[0.873337,0.0003867866,0.1172678,0.0002856532,0.00005754068,0.0002372681,0.001379979,0.0001065141,0.00694149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01353153,"threshold_uncertainty_score":0.02690554,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407051624","doi":"10.1109/ojemb.2025.3537560","title":"Ultrasound Segmentation Using Semi-Supervised Learning: Application in Point-of-Care Sarcopenia Assessment","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Jewish General Hospital; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Segmentation; Ultrasound; Sarcopenia; Point (geometry); Computer science; Artificial intelligence; Medicine; Radiology; Mathematics; Internal medicine","authors":[{"name":"Hamza Rasaee","is_ca":true},{"name":"M. Samuel","is_ca":true},{"name":"Bahareh Behboodi","is_ca":true},{"name":"Jonathan Afilalo","is_ca":true},{"name":"Hassan Rivaz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05326422745903513,"gpt":0.417682814118225,"spread":0.3644185866591899,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002688957,0.0009940454,0.001271431,0.001621695,0.0004751968,0.0008195778,0.001325877,0.001694249,0.0007011035],"category_scores_gemma":[0.005708938,0.0004549762,0.0007062777,0.001015166,0.0006971323,0.0007332234,0.0008894988,0.0008858881,0.0004964439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005209034,"about_ca_system_score_gemma":0.00101932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003856818,"about_ca_topic_score_gemma":0.005295607,"domain_scores_codex":[0.9983783,0.0006498556,0.0001171695,0.0004666434,0.0002886454,0.00009953738],"domain_scores_gemma":[0.9965551,0.001767241,0.0003716111,0.0003348909,0.0008128011,0.0001583439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001046101,0.0007192931,0.01600395,0.0004290777,0.0003364489,0.0004633672,0.0004656173,0.2777468,0.02995447,0.001090852,0.005494835,0.6662493],"study_design_scores_gemma":[0.00002091679,0.0001006898,0.001933301,0.00002263025,0.0000271201,0.000123775,0.00003941223,0.9897168,0.006007571,0.001426476,0.0005626056,0.0000186409],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1483394,0.001824912,0.8423219,0.0005407152,0.0001246458,0.0002246868,0.0004091002,0.00477294,0.001441706],"genre_scores_gemma":[0.7217235,0.0005951357,0.2741339,0.000377249,0.0001638278,0.0001972428,0.001128517,0.0002612869,0.0014194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003856818,"threshold_uncertainty_score":0.01422077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4327662914","doi":"10.1109/ojemb.2023.3257991","title":"EuniceScope: Low-Cost Imaging Platform for Studying Microgravity Cell Biology","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Spaceflight effects on biology","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Reconfigurability; Nanotechnology; Biology; Computer science; Materials science","authors":[{"name":"Wing Yan Chu","is_ca":true},{"name":"Kevin K. Tsia","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06450460627286657,"gpt":0.3705809852514517,"spread":0.3060763789785851,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003351903,0.0003984377,0.0002940191,0.0005023077,0.000267932,0.0004603855,0.0006959862,0.000753417,0.002901071],"category_scores_gemma":[0.0003121075,0.0002713664,0.0002904265,0.0002192818,0.0003185506,0.0005000612,0.000831474,0.0006306806,0.000869109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003334183,"about_ca_system_score_gemma":0.0005814426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005532008,"about_ca_topic_score_gemma":0.000889551,"domain_scores_codex":[0.9997383,0.00002037726,0.00001262645,0.00008053348,0.0001235117,0.00002470088],"domain_scores_gemma":[0.9997935,0.00005294621,0.00004056423,0.00003904426,0.000037519,0.0000365267],"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":[0.00006038378,0.00003246283,0.0004856563,0.0001333446,0.000007917667,0.0001901106,0.0000896314,0.000902537,0.9726188,0.002126938,0.00186688,0.02148533],"study_design_scores_gemma":[0.00004686549,0.0003996165,0.004770322,0.0000649197,0.00003628807,0.00142613,0.00007295978,0.02482069,0.8928163,0.001006722,0.07444587,0.00009330292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2471633,0.004293482,0.7125503,0.0007320071,0.0006003047,0.0004906933,0.001831611,0.009936274,0.02240198],"genre_scores_gemma":[0.3962881,0.002009197,0.5850351,0.0004422598,0.00007204046,0.000895808,0.00132855,0.0004557094,0.01347322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002901071,"threshold_uncertainty_score":0.009705067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320005458","doi":"10.1109/ojemb.2023.3241597","title":"Building a One Country One Licensure Framework: Applications for the Future of Canadian Space Physicians","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Global Health and Surgery","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Western University; University of Windsor","funders":"","keywords":"Licensure; License; Space (punctuation); Health care; Business; Telemedicine; Coronavirus disease 2019 (COVID-19); Public relations; Medicine; Political science; Economic growth; Medical education; Computer science; Economics; Law","authors":[{"name":"Alex Zhou","is_ca":true},{"name":"Valerie Nwaokoro","is_ca":true},{"name":"Valerie Oosterveld","is_ca":true},{"name":"A. Sirek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04417969145057232,"gpt":0.3459495924598406,"spread":0.3017699010092683,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01859105,0.0008174836,0.0004706027,0.004974808,0.01878239,0.0171037,0.00494568,0.006717795,0.03104546],"category_scores_gemma":[0.04603218,0.0006163768,0.001620387,0.004444692,0.006251607,0.009019905,0.008286028,0.005736621,0.005715909],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09397174,"about_ca_system_score_gemma":0.3279361,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9421295,"about_ca_topic_score_gemma":0.969639,"domain_scores_codex":[0.9844861,0.002858011,0.0006221056,0.0009110866,0.007394881,0.003727858],"domain_scores_gemma":[0.9435586,0.005746151,0.001028814,0.001960034,0.03214202,0.01556434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006597693,0.0002236031,0.0136661,0.0004159285,0.00003680474,0.0007586033,0.004990674,0.004586269,0.0006133393,0.3219348,0.4793438,0.1733641],"study_design_scores_gemma":[0.00005212803,0.00007351806,0.007830881,0.001146193,0.00005157106,0.0003357546,0.009571117,0.005980731,0.0004309188,0.04753952,0.9267216,0.0002661628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01818115,0.005479692,0.04936231,0.4284499,0.003869675,0.001129444,0.00256011,0.002751295,0.4882164],"genre_scores_gemma":[0.4306969,0.01346893,0.3432116,0.05690998,0.00137649,0.001124015,0.004232261,0.00100976,0.1479701],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9060283,"threshold_uncertainty_score":0.6818159,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403674583","doi":"10.1109/ojemb.2024.3485535","title":"Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"McGill University","keywords":"Feature (linguistics); Generative adversarial network; Adversarial system; Generative grammar; Computer science; Artificial intelligence; Control (management); Pattern recognition (psychology); Deep learning; Linguistics; Philosophy","authors":[{"name":"James Skoric","is_ca":true},{"name":"Yannick D’Mello","is_ca":true},{"name":"David V. Plant","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04830575407271324,"gpt":0.3097347810693128,"spread":0.2614290269965995,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007062955,0.0007725275,0.0004638015,0.0002602368,0.0001804291,0.0004241543,0.000775238,0.0007680265,0.001759093],"category_scores_gemma":[0.002009453,0.0003737283,0.0006307455,0.0002232903,0.0006656275,0.0004200137,0.0008948015,0.001322806,0.0003785094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006557457,"about_ca_system_score_gemma":0.0005555864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004872362,"about_ca_topic_score_gemma":0.004526766,"domain_scores_codex":[0.9997405,0.00007355204,0.000008552566,0.00008952004,0.00005475508,0.00003305723],"domain_scores_gemma":[0.9994717,0.0003365183,0.00005287953,0.00004647938,0.00005871249,0.00003361759],"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":[0.00005735098,0.00001681472,0.0005611939,0.00001625327,0.00001692621,0.00006174776,0.00002257848,0.9767416,0.001639077,0.004441017,0.001070822,0.01535465],"study_design_scores_gemma":[0.000002532837,0.000005022561,0.00005014097,0.000001566899,0.000001473036,0.000006258712,0.000001066589,0.9986971,0.0003044838,0.0008133682,0.0001150103,0.000001946162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05382078,0.0002797986,0.9409326,0.0005775572,0.0001100458,0.0000792201,0.000314311,0.001015212,0.002870487],"genre_scores_gemma":[0.8885275,0.0001794652,0.1027692,0.000284748,0.00005986742,0.0001557109,0.000842245,0.0001566908,0.007024569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004872362,"threshold_uncertainty_score":0.00968796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404562790","doi":"10.1109/ojemb.2024.3503499","title":"Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Engineering in Medicine and Biology","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Université de Montréal","keywords":"Segmentation; Deep learning; Occlusion; Artificial intelligence; Medicine; Computer science; Internal medicine","authors":[{"name":"Mario Francisco Munoz","is_ca":true},{"name":"Vu Huy Hoang","is_ca":true},{"name":"Thanh-Dung Le","is_ca":true},{"name":"Philippe Jouvet","is_ca":true},{"name":"Rita Noumeir","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03066427264007526,"gpt":0.3511501790170903,"spread":0.3204859063770151,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008907748,0.001032392,0.00121114,0.001239433,0.0003503073,0.0009517659,0.001516907,0.001072619,0.0009251041],"category_scores_gemma":[0.002124421,0.0004934472,0.0008778408,0.0009875039,0.0004157594,0.0007724591,0.001510744,0.0008314458,0.0004695869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034255,"about_ca_system_score_gemma":0.001468466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008388993,"about_ca_topic_score_gemma":0.008964414,"domain_scores_codex":[0.9993336,0.0001100414,0.00003288946,0.0001847732,0.0001946295,0.0001440374],"domain_scores_gemma":[0.9996232,0.0001405562,0.00005954455,0.00005212528,0.0000820652,0.00004240367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009053003,0.0003252184,0.009392188,0.0001740514,0.0001579753,0.0004052665,0.0002539847,0.3555186,0.02504624,0.001403878,0.006947745,0.5994694],"study_design_scores_gemma":[0.000008617325,0.00005215953,0.001272372,0.000008942494,0.00001651841,0.00006899039,0.00001703691,0.9918383,0.005666331,0.0003830576,0.00065916,0.000008500255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1917929,0.001165495,0.7963122,0.0004561043,0.000101784,0.000143921,0.0005382773,0.007351854,0.002137602],"genre_scores_gemma":[0.7203045,0.0006230458,0.2729307,0.0004087572,0.00009321235,0.0001629199,0.002110077,0.0004122345,0.002954466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008388993,"threshold_uncertainty_score":0.01668036,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}