{"id":"W2163136998","doi":"10.1177/0165551508101863","title":"Articulating complex information needs using query templates","year":2009,"lang":"en","type":"article","venue":"Journal of Information Science","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Template; Computer science; Information retrieval; Query expansion; Set (abstract data type); Web search query; Interface (matter); Query language; Web query classification; Information needs; Query optimization; Sargable; Search engine; World Wide Web; Programming language","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":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002332702,0.0001045742,0.0001575156,0.001533998,0.000439828,0.001301363,0.0008795203,0.00004071396,0.00001522138],"category_scores_gemma":[0.0004684838,0.00008076561,0.0000766305,0.002627514,0.0001083437,0.0609206,0.00009465604,0.0001980552,0.00006670642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001761891,"about_ca_system_score_gemma":0.0005318661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004652189,"about_ca_topic_score_gemma":5.879441e-8,"domain_scores_codex":[0.9970374,0.00002319517,0.001166904,0.00003482487,0.001434148,0.0003034929],"domain_scores_gemma":[0.9966441,0.0000437042,0.001075237,0.0002064664,0.001839419,0.0001910631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000861076,0.0001078753,0.002860973,0.00006737373,0.00001760037,0.000006447448,0.05996966,0.05390085,0.031673,0.2062144,0.001505784,0.6435899],"study_design_scores_gemma":[0.000960937,0.000451006,0.07693164,0.00009893275,0.000009687913,0.0008456106,0.003130977,0.8945394,0.01220707,0.001459745,0.009008617,0.0003563567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3832579,0.000003796934,0.6128028,0.0007130366,0.0003057556,0.0001049685,0.000001258527,0.00003869262,0.002771796],"genre_scores_gemma":[0.938144,0.00000395191,0.0603762,0.001437978,0.00003200445,3.113604e-7,0.0000015318,7.639777e-7,0.000003237962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8406386,"threshold_uncertainty_score":0.9997354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04583582339226952,"score_gpt":0.3164522385491602,"score_spread":0.2706164151568907,"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."}}