{"id":"W346258806","doi":"10.1007/978-3-319-16354-3_1","title":"Towards Query Level Resource Weighting for Diversified Query Expansion","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Weighting; Computer science; Query expansion; Resource (disambiguation); Benchmark (surveying); Query optimization; Web query classification; Sargable; Diversification (marketing strategy); Web search query; Information retrieval; Set (abstract data type); Data mining; Search engine","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":[0.002825445,0.001247414,0.002155405,0.002521262,0.0006192434,0.00210431,0.001879275,0.001009723,0.003568756],"category_scores_gemma":[0.008381602,0.0007007355,0.0008104163,0.00366655,0.0007007535,0.004036115,0.002742479,0.001810117,0.001562944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008746844,"about_ca_system_score_gemma":0.001436113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003638185,"about_ca_topic_score_gemma":0.005584165,"domain_scores_codex":[0.9970664,0.0007952979,0.0002423538,0.0004036736,0.001177924,0.0003143136],"domain_scores_gemma":[0.9966916,0.001275428,0.0001412284,0.0008613878,0.0009030125,0.0001273784],"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":[0.00064616,0.0003753873,0.001549884,0.0005106212,0.0001505552,0.0002046771,0.0003181869,0.07004753,0.07305999,0.02983805,0.01773308,0.8055659],"study_design_scores_gemma":[0.0000381182,0.0001066506,0.0005206761,0.00003623219,0.00008754423,0.0002143115,0.0001274246,0.9466929,0.01936925,0.02516219,0.007606857,0.00003786938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01683474,0.001556658,0.977233,0.0002121363,0.00008602167,0.0001400815,0.0002833564,0.00179409,0.001860007],"genre_scores_gemma":[0.2394562,0.00139178,0.7500915,0.0003394368,0.0002414103,0.00028405,0.001552146,0.0005697974,0.006073669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003638185,"threshold_uncertainty_score":0.01494253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07238390860555381,"score_gpt":0.3041685558103236,"score_spread":0.2317846472047698,"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."}}