{"id":"W4400976991","doi":"10.1145/3681784","title":"A Self-Distilled Learning to Rank Model for <i>Ad Hoc</i> Retrieval","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Outlier; Generalizability theory; Artificial intelligence; Rank (graph theory); Sample (material); Learning to rank; Machine learning; Feature (linguistics); Information retrieval; Ranking (information retrieval); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004008277,0.001517605,0.002219577,0.001595712,0.0007240222,0.00251564,0.00344458,0.002115674,0.004223261],"category_scores_gemma":[0.01107359,0.0006411927,0.001307211,0.002144524,0.001442202,0.003804718,0.001726976,0.002508873,0.004066083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122069,"about_ca_system_score_gemma":0.001732349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00611718,"about_ca_topic_score_gemma":0.007016704,"domain_scores_codex":[0.9972436,0.0009573724,0.000203819,0.0005978264,0.0007190891,0.0002782822],"domain_scores_gemma":[0.9948891,0.002186795,0.0005361429,0.001081431,0.001135859,0.000170755],"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":[0.0005397818,0.0004036305,0.00254856,0.0004061915,0.0001616628,0.0001623534,0.000218576,0.5418393,0.005889854,0.03390465,0.01423977,0.3996857],"study_design_scores_gemma":[0.00002406859,0.0001545483,0.000193364,0.00001448101,0.00002459649,0.0000670614,0.00001880004,0.9853395,0.001576416,0.0108799,0.001679546,0.00002768833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01475355,0.000852929,0.9795155,0.0004325807,0.00008336522,0.0001613621,0.0004394627,0.002150202,0.001611046],"genre_scores_gemma":[0.5234619,0.001178366,0.4521293,0.0009299121,0.0005517532,0.0006916148,0.003019389,0.000453176,0.01758454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00611718,"threshold_uncertainty_score":0.02119803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209719530471885,"score_gpt":0.2622081396623819,"score_spread":0.240110944357663,"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."}}