{"id":"W4386942695","doi":"10.48550/arxiv.2309.10910","title":"Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; National Research Council Canada; Mila - Quebec Artificial Intelligence Institute; L'Alliance Boviteq","funders":"Canada Excellence Research Chairs, Government of Canada; National Research Council Canada; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Transfer of learning; Computer science; Machine learning; Artificial intelligence; Task (project management); Spurious relationship; Software deployment; Key (lock)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006844679,0.001618725,0.0008030434,0.001534298,0.0006437065,0.001831735,0.001561316,0.001810906,0.001832352],"category_scores_gemma":[0.02029109,0.0004476814,0.001144325,0.00113841,0.001301977,0.003461051,0.003859998,0.002768833,0.001273686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008331554,"about_ca_system_score_gemma":0.0009954894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002649111,"about_ca_topic_score_gemma":0.003081647,"domain_scores_codex":[0.9980147,0.0008997914,0.00009153685,0.0005616478,0.0002483841,0.0001839173],"domain_scores_gemma":[0.9943242,0.003107694,0.0003839928,0.001367016,0.0006059747,0.0002110874],"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":[0.001231476,0.001244973,0.04004001,0.0004889434,0.0007605283,0.0006566824,0.0007295194,0.3034237,0.04284666,0.004956604,0.009830482,0.5937905],"study_design_scores_gemma":[0.00004905237,0.0002953655,0.009768384,0.00004007366,0.00009626528,0.0002106804,0.0001680738,0.9580297,0.01343111,0.01608034,0.001784583,0.00004635539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5377198,0.002455537,0.4426507,0.002593739,0.000457091,0.0003167987,0.0009781376,0.006840177,0.005987869],"genre_scores_gemma":[0.9504987,0.0003018556,0.0444233,0.0004345771,0.000107333,0.0001311747,0.001708951,0.0001968121,0.002197339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006844679,"threshold_uncertainty_score":0.03619856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2665363899943396,"score_gpt":0.2567135652604284,"score_spread":0.009822824733911217,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}