{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001841901,0.0002114084,0.0002546615,0.00007186689,0.0002837119,0.0001029954,0.0007259413,0.0001172345,0.0002236934],"category_scores_gemma":[0.000144,0.0001949837,0.00005132646,0.0003593484,0.0001641026,0.0003892831,0.0005755902,0.000214911,0.0001763175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009295505,"about_ca_system_score_gemma":0.0001726306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005102874,"about_ca_topic_score_gemma":0.00001831666,"domain_scores_codex":[0.9982226,0.00005184608,0.0002894713,0.0006125738,0.0004079611,0.0004154734],"domain_scores_gemma":[0.9986666,0.000108442,0.00009931781,0.0007326321,0.0001731833,0.0002197726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001190379,0.001103843,0.2230752,0.0003077807,0.0004069713,0.002774865,0.03284625,0.0004026659,0.05100074,0.4734125,0.05661174,0.1568671],"study_design_scores_gemma":[0.006713044,0.001390417,0.3899671,0.0002911745,0.00006639794,0.003029998,0.0002222129,0.4193059,0.03930353,0.01295476,0.1224959,0.004259659],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6383957,0.0004090928,0.3530188,0.001034304,0.0003100006,0.0001838987,0.00002075076,0.0002580465,0.006369414],"genre_scores_gemma":[0.9928614,0.0001138552,0.004391847,0.0002002395,0.00004200975,0.000002707385,0.00000908904,0.000009147306,0.00236969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4604577,"threshold_uncertainty_score":0.7951206,"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."}}