{"id":"W2142564378","doi":"","title":"Distributed EDLSI, BM25, and Power Norm at TREC 2008","year":2008,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Normalization (sociology); Weighting; Information retrieval; Search engine indexing; Vector space model; Data mining; Relevance feedback; Relevance (law); Artificial intelligence; Image retrieval","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":[0.02397801,0.002802207,0.002992799,0.004521294,0.002570544,0.002980381,0.004226251,0.002513778,0.006895222],"category_scores_gemma":[0.03275941,0.0006501659,0.001302348,0.003851366,0.001357484,0.004191761,0.00314479,0.003942295,0.00541773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005925972,"about_ca_system_score_gemma":0.004028827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02424586,"about_ca_topic_score_gemma":0.03495745,"domain_scores_codex":[0.9804387,0.006965431,0.00117056,0.003455124,0.006877926,0.001092311],"domain_scores_gemma":[0.9806974,0.004647954,0.0007798347,0.004880921,0.007594686,0.001399185],"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":[0.004258039,0.003750617,0.007264738,0.00141272,0.0007542355,0.0002616449,0.0004359516,0.06414228,0.02058197,0.004117006,0.3172338,0.575787],"study_design_scores_gemma":[0.004223268,0.005425519,0.03656245,0.000215459,0.0004951401,0.0005405627,0.0008396891,0.7540265,0.108161,0.01308731,0.07574192,0.000681119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.506036,0.01432647,0.2505009,0.008222388,0.007265791,0.008748543,0.05249018,0.06644563,0.08596397],"genre_scores_gemma":[0.6465876,0.0008632124,0.2401095,0.001303725,0.001052751,0.003308867,0.0809052,0.001925602,0.02394365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02424586,"threshold_uncertainty_score":0.1268094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03705805193682006,"score_gpt":0.2425151001995324,"score_spread":0.2054570482627124,"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."}}