{"id":"W4415345326","doi":"10.48550/arxiv.2507.03922","title":"Dynamic Injection of Entity Knowledge into Dense Retrievers","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Code (set theory); Layer (electronics); Labrador Retriever; Set (abstract data type)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002350542,0.00202572,0.001554238,0.002934297,0.0006312139,0.002069782,0.003239215,0.001792056,0.00535242],"category_scores_gemma":[0.0144444,0.0006885824,0.001152587,0.002902025,0.001131621,0.009895802,0.004299543,0.002112712,0.005935676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007213214,"about_ca_system_score_gemma":0.0009205249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004868962,"about_ca_topic_score_gemma":0.009180686,"domain_scores_codex":[0.9984628,0.0002890076,0.0001418567,0.000510539,0.0004536454,0.000142209],"domain_scores_gemma":[0.9948996,0.001966392,0.0002988192,0.002002799,0.0006537276,0.0001786208],"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":[0.001160342,0.000974268,0.01485776,0.001404376,0.0004977055,0.001322675,0.001174738,0.07629345,0.04809779,0.01027797,0.08936086,0.7545781],"study_design_scores_gemma":[0.0001791244,0.0007180593,0.004888616,0.0001183265,0.0003479487,0.001499652,0.0006539396,0.855795,0.05281737,0.02923124,0.05357305,0.0001775996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1754326,0.005254623,0.6945788,0.001985024,0.0005054924,0.0008135427,0.01248399,0.09514051,0.01380536],"genre_scores_gemma":[0.6142779,0.001737623,0.3355721,0.001321812,0.0004025773,0.0003358187,0.02891674,0.002693144,0.01474236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00535242,"threshold_uncertainty_score":0.01790559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382128318680259,"score_gpt":0.2891982362677081,"score_spread":0.2653769530809055,"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."}}