{"id":"W2116133147","doi":"10.1145/2348283.2348289","title":"Adaptive query suggestion for difficult queries","year":2012,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Web query classification; Web search query; Spatial query; Information retrieval; Query expansion; Query optimization; Sargable; Query language; Search engine; Rank (graph theory); RDF query language; Range query (database); Similarity (geometry); Data mining; Artificial intelligence; 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":[0.003729976,0.001392722,0.00151827,0.002248697,0.0008045909,0.001469817,0.001361517,0.001559376,0.003005026],"category_scores_gemma":[0.03775426,0.0003650131,0.0005931547,0.001729055,0.0005734633,0.0027129,0.0009535975,0.001455646,0.001871769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000439717,"about_ca_system_score_gemma":0.001051592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559673,"about_ca_topic_score_gemma":0.00350039,"domain_scores_codex":[0.994501,0.00249484,0.0003797821,0.0006463461,0.001643137,0.0003349016],"domain_scores_gemma":[0.9613429,0.02670187,0.00261314,0.00371447,0.004810325,0.0008172797],"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":[0.004204824,0.001625102,0.05564103,0.002865436,0.0004830465,0.00158121,0.002265635,0.03802436,0.1562903,0.004025945,0.03400696,0.6989862],"study_design_scores_gemma":[0.0007153773,0.004436462,0.07133515,0.000308698,0.00109622,0.005636261,0.002126792,0.7570724,0.08401224,0.01273029,0.05990298,0.0006269751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6015334,0.00904104,0.3509018,0.002469168,0.0003705718,0.001708186,0.001061303,0.02071445,0.01220011],"genre_scores_gemma":[0.8558096,0.0006953897,0.1383194,0.0004173152,0.0002489566,0.000245532,0.0008390608,0.0004652252,0.002959614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003729976,"threshold_uncertainty_score":0.01972622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612072071637346,"score_gpt":0.2763993079525072,"score_spread":0.2402785872361337,"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."}}