{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":7,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":7,"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":"112a83e5bde3","filters":{"venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)"}},"results":[{"id":"W4224994441","doi":"10.1109/isbi52829.2022.9761421","title":"Addformer: Alzheimer’s Disease Detection from Structural Mri Using Fusion Transformer","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":57,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"ALS Society of Canada","keywords":"Computer science; Leverage (statistics); Artificial intelligence; Cognitive impairment; Dementia; Contextual image classification; Transfer of learning; Machine learning; Deep learning; Transformer; Benchmark (surveying); Pattern recognition (psychology); Disease; Image (mathematics); Medicine; Pathology","authors":[{"name":"Rafsanjany Kushol","is_ca":true},{"name":"Abbas Masoumzadeh","is_ca":true},{"name":"Huo Dong","is_ca":true},{"name":"Sanjay Kalra","is_ca":true},{"name":"Yee‐Hong Yang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02962323558783755,"gpt":0.2902625069079087,"spread":0.2606392713200711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007023424,0.001460284,0.0007871985,0.002033065,0.0003352465,0.0008501804,0.001273627,0.0009092985,0.0042831],"category_scores_gemma":[0.001409048,0.0003755084,0.0009053473,0.0008847399,0.0002721793,0.0009408703,0.001481097,0.0007374191,0.003091612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000391902,"about_ca_system_score_gemma":0.000724067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003828635,"about_ca_topic_score_gemma":0.008740367,"domain_scores_codex":[0.9997372,0.00003319703,0.00001591437,0.00007514338,0.00009751169,0.00004101],"domain_scores_gemma":[0.9997844,0.00004536746,0.00002419829,0.00004952676,0.00007048718,0.00002607267],"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":[0.0006839464,0.000239804,0.006608411,0.0003247978,0.0003173529,0.000594113,0.00008403387,0.01484209,0.0520761,0.001845038,0.04442946,0.8779548],"study_design_scores_gemma":[0.0002134876,0.0005271366,0.01316919,0.0001117393,0.0004520276,0.007107612,0.0001720411,0.8271079,0.1072522,0.01509652,0.02865326,0.0001366884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06231597,0.002745447,0.8958751,0.0005872762,0.0003426672,0.0004363715,0.005992206,0.02589879,0.005806164],"genre_scores_gemma":[0.4464403,0.002421859,0.5147672,0.000752472,0.0003137086,0.0004407191,0.0211194,0.001134653,0.01260968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0042831,"threshold_uncertainty_score":0.01432842,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224988658","doi":"10.1109/isbi52829.2022.9761550","title":"Semi-Supervised Tumor Response Grade Classification from Histology Images of Colorectal Liver Metastases","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Colorectal cancer; Pattern recognition (psychology); Segmentation; Contextual image classification; Medicine; Cancer; Internal medicine; Image (mathematics)","authors":[{"name":"Mohamed El Amine Elforaici","is_ca":true},{"name":"Emmanuel Montagnon","is_ca":true},{"name":"Féryel Azzi","is_ca":true},{"name":"Dominique Trudel","is_ca":true},{"name":"Bich Nguyen","is_ca":true},{"name":"Simon Turcotte","is_ca":true},{"name":"An Tang","is_ca":true},{"name":"Samuel Kadoury","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01796221590008288,"gpt":0.2672617231353203,"spread":0.2492995072352374,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006136692,0.0005966941,0.0005548011,0.0007328709,0.0001634381,0.0003859508,0.0006589476,0.0006153244,0.0007554614],"category_scores_gemma":[0.001615484,0.0001809699,0.0006234875,0.0003046894,0.0002408704,0.0002764334,0.0003545164,0.0004639832,0.0007517852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000293677,"about_ca_system_score_gemma":0.0004275915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002666788,"about_ca_topic_score_gemma":0.007431267,"domain_scores_codex":[0.9996336,0.00007788523,0.00002641429,0.0001333242,0.000077626,0.00005124623],"domain_scores_gemma":[0.9990516,0.0003034481,0.0001702964,0.0001394332,0.0002742519,0.00006094162],"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":[0.001609214,0.0007957252,0.06173705,0.0005100208,0.0003230218,0.0006983122,0.0003903241,0.08547813,0.1286308,0.000529714,0.01109403,0.7082037],"study_design_scores_gemma":[0.00004490556,0.0004577768,0.0514333,0.00003951591,0.0001024312,0.0007952659,0.0001326216,0.8928316,0.04958086,0.001256568,0.003287602,0.00003753871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7050397,0.001258909,0.2798477,0.0004047164,0.0001269022,0.0003085477,0.002890771,0.007189295,0.002933392],"genre_scores_gemma":[0.9428325,0.0002073655,0.04844437,0.0001001289,0.00006072406,0.0001164682,0.00494772,0.0001457847,0.003145041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002666788,"threshold_uncertainty_score":0.005302489,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4225004743","doi":"10.1109/isbi52829.2022.9761663","title":"Lightseg: Efficient Yet Effective Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"COVID-19 diagnosis using AI","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":"Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Computer science; Segmentation; Image segmentation; Artificial intelligence; Market segmentation; Deep learning; Scale-space segmentation; Segmentation-based object categorization; Separable space; Computer vision; Image (mathematics); Pattern recognition (psychology); Machine learning; Mathematics","authors":[{"name":"Most Husne Jahan","is_ca":true},{"name":"Abdullah-Al-Zubaer Imran","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008831438168704917,"gpt":0.315245715190075,"spread":0.3064142770213701,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008783797,0.001361899,0.0008587075,0.001800429,0.0003712169,0.001344796,0.002093816,0.001745378,0.006240827],"category_scores_gemma":[0.002056313,0.000752948,0.0009335343,0.0008773968,0.0005539084,0.001420337,0.002259767,0.001326582,0.002989433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008261151,"about_ca_system_score_gemma":0.001396669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00364386,"about_ca_topic_score_gemma":0.006402787,"domain_scores_codex":[0.9995362,0.00007053973,0.00002550182,0.0001233251,0.0001826436,0.00006184906],"domain_scores_gemma":[0.9996197,0.0001387675,0.00004305966,0.00007285034,0.00007692864,0.00004867614],"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":[0.001126847,0.0002519504,0.001748059,0.000307514,0.0002000969,0.000416235,0.0001114786,0.08751926,0.05350678,0.006609998,0.04101222,0.8071895],"study_design_scores_gemma":[0.0001367099,0.0001635091,0.0008050899,0.00003999141,0.00004276106,0.0004989089,0.00003650995,0.9294435,0.04530276,0.01054067,0.01294496,0.0000446712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02344124,0.001203005,0.9362806,0.0008404085,0.0001836508,0.0003381355,0.001375324,0.0324607,0.003876929],"genre_scores_gemma":[0.1853342,0.0008710762,0.796546,0.001385078,0.0001629399,0.0002895699,0.005397361,0.001941212,0.0080725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006240827,"threshold_uncertainty_score":0.0208776,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224990359","doi":"10.1109/isbi52829.2022.9761434","title":"Symmetric Contrastive Loss for Out-of-Distribution Skin Lesion Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Variance (accounting); Adversarial system; Task (project management); Class (philosophy); Machine learning; Pattern recognition (psychology); Scale (ratio)","authors":[{"name":"Xuan Li","is_ca":true},{"name":"Christian Desrosiers","is_ca":true},{"name":"Xue Liu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01516732637706618,"gpt":0.28595787258164,"spread":0.2707905462045738,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002852668,0.000906121,0.0007822258,0.0007854428,0.0002814888,0.0007526259,0.001311244,0.000999196,0.001052314],"category_scores_gemma":[0.006033565,0.0002352152,0.0004956297,0.0004924529,0.00108443,0.001389236,0.001276618,0.00157982,0.0005019603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008149663,"about_ca_system_score_gemma":0.0007207927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00108123,"about_ca_topic_score_gemma":0.001363739,"domain_scores_codex":[0.9990205,0.0002647588,0.00004062579,0.0002391032,0.0003189638,0.0001161477],"domain_scores_gemma":[0.9978349,0.001103859,0.0002564482,0.0003700503,0.0003262856,0.0001084489],"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":[0.001129342,0.0007480787,0.01347278,0.0002722052,0.0002195288,0.0005472847,0.0001539201,0.4013316,0.05765175,0.01225988,0.0171567,0.4950569],"study_design_scores_gemma":[0.00001451992,0.000134382,0.002100444,0.00001253857,0.00001554308,0.0002264124,0.00001688354,0.9819843,0.01072355,0.003608362,0.001151689,0.00001132634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1409898,0.001126067,0.8511122,0.0009933872,0.0001453448,0.000146135,0.0004314709,0.001522207,0.003533282],"genre_scores_gemma":[0.8872122,0.0004366494,0.1065634,0.0006938566,0.0001801931,0.0000931075,0.000810808,0.0001765664,0.00383315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002852668,"threshold_uncertainty_score":0.01508653,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3187359846","doi":"10.1109/isbi52829.2022.9761680","title":"Reproducibility and Evolution of Diffusion Mri Measurements Within the Cervical Spinal Cord in Multiple Sclerosis","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"Advanced Neuroimaging 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":"Polytechnique Montréal","funders":"EMI","keywords":"Multiple sclerosis; Spinal cord; Diffusion MRI; Reproducibility; Diffusion; Magnetic resonance imaging; Medicine; Computer science; Radiology; Physics; Mathematics","authors":[{"name":"Haykel Snoussi","is_ca":false},{"name":"Benoît Combès","is_ca":false},{"name":"Olivier Commowick","is_ca":false},{"name":"Élise Bannier","is_ca":false},{"name":"Anne Kerbrat","is_ca":false},{"name":"Julien Cohen‐Adad","is_ca":true},{"name":"Christian Barillot","is_ca":false},{"name":"Emmanuel Caruyer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09399315719702295,"gpt":0.3408187529118911,"spread":0.2468255957148682,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004668616,0.0004991562,0.0004111963,0.001183975,0.0004290765,0.0007689235,0.0005222443,0.000788834,0.0003213976],"category_scores_gemma":[0.02740302,0.0002198972,0.0003392727,0.0006952042,0.0007613802,0.0004663701,0.0007002726,0.0003937452,0.0002295914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003357292,"about_ca_system_score_gemma":0.0002425363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003745999,"about_ca_topic_score_gemma":0.00438489,"domain_scores_codex":[0.9971153,0.001021463,0.0002991358,0.0008756418,0.0005649036,0.0001236118],"domain_scores_gemma":[0.9841731,0.00696184,0.00211252,0.003510666,0.002903503,0.0003383645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003337776,0.0002244487,0.7053815,0.0005239059,0.00119339,0.0007527304,0.003978955,0.01389468,0.1519487,0.00035677,0.000699493,0.1177076],"study_design_scores_gemma":[0.00003063914,0.001051545,0.9428383,0.00003926939,0.000295641,0.001826146,0.0005178911,0.01649691,0.03548244,0.0005091405,0.0008240974,0.00008808162],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912164,0.0007065416,0.007034872,0.00005201828,0.00002862158,0.00002220456,0.0002333898,0.0001215519,0.0005844878],"genre_scores_gemma":[0.9978241,0.00006917922,0.001737209,0.00001099614,0.00001115911,0.000009008842,0.0001963474,0.00002830003,0.0001137284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004668616,"threshold_uncertainty_score":0.02469027,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224985423","doi":"10.1109/isbi52829.2022.9761425","title":"Leveraging Multi-Visit Information for Magnetic Resonance Image Reconstruction: Pilot Study on a Cohort of Glioblastoma Subjects","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hotchkiss Brain Institute; Artificial Intelligence in Medicine (Canada); University of Calgary","funders":"","keywords":"Magnetic resonance imaging; Artificial intelligence; Computer science; Iterative reconstruction; Similarity (geometry); Glioblastoma; Peak signal-to-noise ratio; Cohort; Deep learning; Computer vision; Signal-to-noise ratio (imaging); Medicine; Image (mathematics); Medical physics; Pattern recognition (psychology); Radiology; Pathology","authors":[{"name":"Youssef Beauferris","is_ca":true},{"name":"Mike Lasby","is_ca":true},{"name":"Roberto Martins de Souza","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01956657289193005,"gpt":0.3003291153915793,"spread":0.2807625424996493,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001222223,0.0005375266,0.000628704,0.0004109645,0.0003259205,0.0004277158,0.0003846023,0.0005134441,0.001085283],"category_scores_gemma":[0.002910681,0.0002207913,0.0005446528,0.0002912079,0.000283704,0.0003500881,0.0004013697,0.0005168599,0.0005119364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605638,"about_ca_system_score_gemma":0.0005044083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007004389,"about_ca_topic_score_gemma":0.009808256,"domain_scores_codex":[0.9997734,0.00008289858,0.00001362771,0.00007329111,0.00002967584,0.00002709672],"domain_scores_gemma":[0.9991332,0.0002757111,0.00009577937,0.0001791284,0.0001858726,0.0001303433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007703669,0.004058563,0.7790818,0.0002440401,0.001149496,0.003404827,0.001288871,0.01325272,0.02644158,0.0003003147,0.00716549,0.1559086],"study_design_scores_gemma":[0.0007982839,0.01065536,0.7972216,0.0001034632,0.001635746,0.009236481,0.001999191,0.1514912,0.01605328,0.001978846,0.008629412,0.0001972128],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955473,0.0001461552,0.003172048,0.0001219064,0.00001641031,0.00004533077,0.0007125352,0.0000712436,0.0001670981],"genre_scores_gemma":[0.992334,0.000162915,0.00472485,0.00007026842,0.00004400801,0.00005073945,0.002044002,0.00004248417,0.0005268032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007004389,"threshold_uncertainty_score":0.01392722,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224999590","doi":"10.1109/isbi52829.2022.9761413","title":"Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institute of Cancer Research","funders":"Breast Cancer Now","keywords":"Ductal carcinoma; Artificial intelligence; Breast cancer; Computer science; Convolutional neural network; Multiplex; Deep learning; Pattern recognition (psychology); Segmentation; Immunohistochemistry; Pathology; Cancer; Medicine; Biology; Internal medicine; Bioinformatics","authors":[{"name":"Faranak Sobhani","is_ca":true},{"name":"Azam Hamidinekoo","is_ca":true},{"name":"Allison Hall","is_ca":false},{"name":"Lorraine King","is_ca":false},{"name":"Jeffrey R. Marks","is_ca":false},{"name":"Carlo C. Maley","is_ca":false},{"name":"Hugo M. Horlings","is_ca":true},{"name":"E. Shelley Hwang","is_ca":false},{"name":"Yinyin Yuan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01221406495269541,"gpt":0.2812345968520643,"spread":0.2690205318993689,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001160969,0.0008216555,0.000506092,0.0007800522,0.0002147265,0.0006576599,0.0008135782,0.000702649,0.001277041],"category_scores_gemma":[0.00138662,0.0004632748,0.0007666945,0.0003161767,0.0004969787,0.0004483923,0.0009216039,0.0008505045,0.000482358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007991445,"about_ca_system_score_gemma":0.0005207314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00247539,"about_ca_topic_score_gemma":0.003480342,"domain_scores_codex":[0.9996934,0.00006887304,0.00001369186,0.00009653047,0.00008240342,0.00004512158],"domain_scores_gemma":[0.9993407,0.0003476557,0.00009385993,0.00009824623,0.00008736367,0.00003222496],"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":[0.0002836859,0.00008020241,0.004423442,0.00008591245,0.0001039811,0.0002819654,0.00008751357,0.7709477,0.05259612,0.003352878,0.00202296,0.1657337],"study_design_scores_gemma":[0.000001957344,0.0000116467,0.0004151382,0.000002815104,0.000004395773,0.00004216461,0.000003872134,0.9938834,0.004398351,0.001015972,0.0002163386,0.000004108439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07055995,0.0002758975,0.9255294,0.0001934936,0.00003725135,0.00008988674,0.0002143508,0.002038072,0.00106171],"genre_scores_gemma":[0.7423292,0.0003029764,0.2522913,0.0002159315,0.0000451637,0.0001349658,0.0008823622,0.0001871955,0.00361102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00247539,"threshold_uncertainty_score":0.006139815,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}