{"id":"W2056769738","doi":"10.1145/2490255","title":"A vlHMM approach to context-aware search","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on the Web","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Microsoft Research","keywords":"Computer science; Information retrieval; Session (web analytics); Context (archaeology); Web search query; Feature (linguistics); Construct (python library); Search engine; Data mining; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001127214,0.0006629621,0.001303707,0.001482356,0.0005648526,0.0007747547,0.00211641,0.001215523,0.001625067],"category_scores_gemma":[0.004997739,0.0006768377,0.00130405,0.001713563,0.0006715575,0.002064249,0.001158274,0.002032708,0.0005803113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00138195,"about_ca_system_score_gemma":0.001526575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01278217,"about_ca_topic_score_gemma":0.01755629,"domain_scores_codex":[0.9991179,0.0002862412,0.00006023483,0.0002787635,0.0001711647,0.00008566061],"domain_scores_gemma":[0.9981894,0.001158293,0.0001355753,0.0002652808,0.0001954438,0.00005610286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002467211,0.0003107617,0.004595447,0.0002520621,0.0002573019,0.0001814488,0.000384571,0.674506,0.00534632,0.04130072,0.004352116,0.2682665],"study_design_scores_gemma":[0.000009571445,0.00002847603,0.000251852,0.000005967899,0.00001456062,0.00002371631,0.00001051576,0.9840385,0.0003657929,0.01469149,0.0005487973,0.00001080812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01608807,0.000690536,0.9809219,0.0003655165,0.000044554,0.00005362157,0.0002475351,0.0009120174,0.0006762212],"genre_scores_gemma":[0.6370618,0.0007946009,0.356737,0.0004468441,0.000224204,0.0003781667,0.0009512844,0.0001338445,0.003272259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01278217,"threshold_uncertainty_score":0.02541554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.045809388665741,"score_gpt":0.2591206228227113,"score_spread":0.2133112341569703,"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."}}