{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1,"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":"3caefebcbd0e","filters":{"venue":"International Journal of Bioinformatics and Intelligent Computing"}},"results":[{"id":"W4388709846","doi":"10.61797/ijbic.v1i2.153","title":"3D Multimodal Brain Tumor Segmentation and Grading Scheme based on Machine, Deep, and Transfer Learning Approaches","year":2022,"lang":"en","type":"article","venue":"International Journal of Bioinformatics and Intelligent Computing","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":7,"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":"National Cancer Institute; National Institutes of Health","keywords":"Computer science; Grading (engineering); Artificial intelligence; Segmentation; Deep learning; Transfer of learning; Glioma; Machine learning; Brain tumor; Support vector machine; Pattern recognition (psychology); Engineering","authors":[{"name":"Erdal Taşçı","is_ca":false},{"name":"Aybars Uğur","is_ca":false},{"name":"Kevin Camphausen","is_ca":false},{"name":"Ying Zhuge","is_ca":false},{"name":"Rachel Zhao","is_ca":true},{"name":"Andra Krauze","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03879732224959039,"gpt":0.2655185485211727,"spread":0.2267212262715824,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005417546,0.001242883,0.0007836426,0.00289875,0.0004302046,0.001220366,0.001209447,0.001021899,0.002134608],"category_scores_gemma":[0.001015989,0.0004269231,0.001168774,0.001112221,0.0003496576,0.0009618935,0.001158241,0.0007068635,0.0009767951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009593328,"about_ca_system_score_gemma":0.001117687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008624847,"about_ca_topic_score_gemma":0.01232522,"domain_scores_codex":[0.9995766,0.0000411871,0.00003724736,0.0001230798,0.000155434,0.00006648056],"domain_scores_gemma":[0.9997404,0.00003023589,0.00003154817,0.00003943029,0.0001282365,0.00003007002],"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":[0.000310747,0.0001482432,0.00601032,0.0001573266,0.0001085078,0.000225356,0.0001457547,0.08549376,0.03611242,0.003448406,0.006876184,0.8609629],"study_design_scores_gemma":[0.00001687243,0.00007644204,0.002288412,0.00002191035,0.0000555871,0.0002521372,0.00004579706,0.9700012,0.02121792,0.002966273,0.003023804,0.00003368841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05891412,0.0007138242,0.9314336,0.0002839261,0.00007111955,0.0002999647,0.0007263449,0.004785454,0.002771567],"genre_scores_gemma":[0.4684904,0.0007796544,0.5217898,0.0002800805,0.00007321777,0.0003031613,0.003034858,0.0003749638,0.0048738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008624847,"threshold_uncertainty_score":0.01714927,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}