{"id":"W4410730191","doi":"10.1007/978-3-031-91585-7_7","title":"TF-OCM: Training-Free Optimal Community Matching for Domain Generalized Few-Shot Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Shot (pellet); Matching (statistics); Domain (mathematical analysis); Artificial intelligence; Training (meteorology); Pattern recognition (psychology); Machine learning; Statistics; Mathematics","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":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003958283,0.0008166589,0.0009700618,0.001188014,0.001780866,0.00133858,0.006697453,0.0004850813,0.0000376859],"category_scores_gemma":[0.0006267363,0.0008327203,0.0003542121,0.000830396,0.0006833217,0.0007575995,0.00274269,0.002934415,0.00001670244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004152928,"about_ca_system_score_gemma":0.001018263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007372554,"about_ca_topic_score_gemma":0.0001304637,"domain_scores_codex":[0.9949328,0.0003536983,0.0009033434,0.001541385,0.001105663,0.001163161],"domain_scores_gemma":[0.9943644,0.002369324,0.0005571179,0.002074118,0.0003566186,0.0002784577],"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":[0.00004669765,0.00005417506,0.00001615885,0.0001447289,0.00005425703,0.00004945293,0.01796273,0.3272227,0.000458296,0.1609942,0.0001790923,0.4928174],"study_design_scores_gemma":[0.001756387,0.0003677688,0.00005190267,0.0007349744,0.00002246124,0.00006891457,0.00002284859,0.6470221,0.0002347357,0.3263338,0.02210721,0.00127694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004459833,0.0002669325,0.9846819,0.001545226,0.001784633,0.000732783,0.00001209288,0.0004640916,0.01006637],"genre_scores_gemma":[0.04395838,0.00002734539,0.9498186,0.002826372,0.0004118698,0.00004741271,0.00003780686,0.00006677367,0.002805468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4915405,"threshold_uncertainty_score":0.9996981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04717910218694317,"score_gpt":0.2868922300215428,"score_spread":0.2397131278345997,"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."}}