{"id":"W1816957470","doi":"10.48550/arxiv.0908.2588","title":"Wild Card Queries for Searching Resources on the Web","year":2009,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Web query classification; Information retrieval; Web search query; Unary operation; Ranking (information retrieval); Query expansion; Tuple; Set (abstract data type); Sargable; Query optimization; Task (project management); Query language; WordNet; RDF query language; Search engine; Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000993798,0.0002543366,0.0003224713,0.0001440199,0.0004032119,0.0004758448,0.002394049,0.0001297178,0.00000562146],"category_scores_gemma":[0.0002954762,0.0001739179,0.0002728853,0.0002175931,0.00008254917,0.0001396165,0.001176514,0.0006330874,0.00006144243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003934168,"about_ca_system_score_gemma":0.0001267922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001395793,"about_ca_topic_score_gemma":0.00005060268,"domain_scores_codex":[0.998099,0.0001922421,0.0002556097,0.000741563,0.0003466713,0.0003648661],"domain_scores_gemma":[0.9974889,0.000466217,0.0001692969,0.001726145,0.00007128865,0.00007814319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002603483,0.0007389547,0.2762298,0.0008420544,0.002798226,0.000196415,0.04578637,0.0174669,0.00314898,0.1076229,0.2469808,0.2979283],"study_design_scores_gemma":[0.001010298,0.001012065,0.1213605,0.002422557,0.00044606,0.00001865406,0.001762568,0.1538763,0.005696531,0.0233915,0.6858967,0.003106296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589487,0.0002675857,0.02053182,0.01688474,0.0003420852,0.0002557429,0.00005933331,0.0002555593,0.00245443],"genre_scores_gemma":[0.9914714,0.00009140011,0.005423527,0.001404306,0.0004706089,0.00005581245,0.00003603664,0.00001832342,0.001028589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4389159,"threshold_uncertainty_score":0.7092165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07159459653999371,"score_gpt":0.2893932437194094,"score_spread":0.2177986471794157,"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."}}