{"id":"W7082265321","doi":"10.48448/qh4p-k287","title":"Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection","year":2025,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Geodetic Measurements and Engineering Structures","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Selection (genetic algorithm); Supervised learning; Feature selection; Disease; Language model; Training set","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002593337,0.0002160784,0.0001829683,0.0009235906,0.00007820005,0.00007552297,0.000161022,0.0001374021,0.00003340165],"category_scores_gemma":[0.0001485709,0.0002302026,0.00003248375,0.0005886251,0.0000519418,0.0001190017,0.0000183495,0.0002919383,0.000002500128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002044349,"about_ca_system_score_gemma":0.0001704412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004236678,"about_ca_topic_score_gemma":0.00478346,"domain_scores_codex":[0.9988687,0.00001276267,0.0002510258,0.0002935524,0.0002220006,0.0003519299],"domain_scores_gemma":[0.9997029,0.00002303449,0.00005218914,0.0001029399,0.00004821,0.00007073511],"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":[0.00001320391,0.00001872354,0.0007481459,0.0002790554,0.00002977435,0.000001698049,0.0001170622,0.5888931,0.007602387,0.0004235703,0.0005299295,0.4013433],"study_design_scores_gemma":[0.0004188185,0.00003079965,0.003891809,0.0002103769,0.00004434767,0.00000111882,0.0001043301,0.9887719,0.00155622,0.0002466172,0.004392569,0.000331167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01571772,0.005355355,0.939516,0.0001081677,0.004325226,0.002982248,0.00003859596,0.00181477,0.0301419],"genre_scores_gemma":[0.9964121,0.00002621933,0.001847818,0.00001412876,0.000124584,0.0000660938,0.00001597511,0.00008909895,0.001403958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9806944,"threshold_uncertainty_score":0.9387388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160467984471793,"score_gpt":0.2354778939802408,"score_spread":0.2238732141355229,"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."}}