{"id":"W4409365527","doi":"10.1609/aaai.v39i11.33306","title":"RouterRetriever: Routing over a Mixture of Expert Embedding Models","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Embedding; Computer science; Routing (electronic design automation); Artificial intelligence; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003252151,0.002417905,0.001487255,0.001919191,0.0005963776,0.001713719,0.003140606,0.002649164,0.005236325],"category_scores_gemma":[0.008974557,0.0009488378,0.001806936,0.001311462,0.0006757317,0.006773741,0.002431554,0.002711867,0.005164626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423095,"about_ca_system_score_gemma":0.00126894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005897325,"about_ca_topic_score_gemma":0.009288092,"domain_scores_codex":[0.9984142,0.0005485723,0.00010704,0.0005475557,0.0002626908,0.0001200163],"domain_scores_gemma":[0.9970988,0.0013217,0.0001610535,0.0009595256,0.0003427384,0.0001162216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008899194,0.0006460351,0.003780077,0.0007549948,0.0005140135,0.0004678285,0.0004079235,0.2452603,0.01717404,0.01408559,0.06371497,0.6523044],"study_design_scores_gemma":[0.00007813091,0.0002119719,0.0002167689,0.00002265698,0.00007296491,0.0002333893,0.00005612262,0.9763619,0.005798073,0.01023026,0.00667903,0.00003869443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04557912,0.002228084,0.9108145,0.001081725,0.0001909973,0.0006035262,0.002195883,0.03243136,0.004874799],"genre_scores_gemma":[0.3484384,0.001047338,0.6225668,0.001720931,0.0002360383,0.0007370578,0.009653064,0.001866392,0.01373388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005897325,"threshold_uncertainty_score":0.01751727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04657043587603436,"score_gpt":0.3282586354173401,"score_spread":0.2816881995413058,"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."}}