{"id":"W7096632815","doi":"","title":"Using various indexing schemes and multiple translations","year":2005,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clef; Search engine indexing; Document retrieval; Scheme (mathematics); Task (project management); Weighting; Component (thermodynamics)","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":[0.006313135,0.001834971,0.002029946,0.003036488,0.001406832,0.002476886,0.001828554,0.00112102,0.006031535],"category_scores_gemma":[0.02870209,0.0007509957,0.001095369,0.005481627,0.0009566626,0.005218482,0.002130046,0.001255061,0.003404209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001541772,"about_ca_system_score_gemma":0.001519941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004450555,"about_ca_topic_score_gemma":0.008121989,"domain_scores_codex":[0.9918596,0.003804506,0.001379331,0.001219421,0.001306981,0.0004300758],"domain_scores_gemma":[0.978127,0.01088983,0.000938875,0.005209142,0.004387033,0.0004480739],"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":[0.007144116,0.001763454,0.008073221,0.005754938,0.001159136,0.0005542812,0.002022814,0.02986168,0.1226032,0.006531294,0.01831053,0.7962215],"study_design_scores_gemma":[0.004436752,0.007769974,0.01778409,0.0005665421,0.00470134,0.002141241,0.0029911,0.3725523,0.4980903,0.02125249,0.06665463,0.001059222],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.777254,0.00857397,0.173214,0.001189647,0.0008118357,0.001923429,0.005604011,0.01735347,0.01407563],"genre_scores_gemma":[0.6014958,0.001683269,0.3762293,0.000271665,0.0002142394,0.0008070589,0.009776874,0.002158257,0.007363483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006313135,"threshold_uncertainty_score":0.03338742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0632459045275825,"score_gpt":0.3055839517645167,"score_spread":0.2423380472369343,"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."}}