{"id":"W2787850647","doi":"10.1145/3161607","title":"Modeling Queries with Contextual Snippets for Information Retrieval","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Information retrieval; Query expansion; Context (archaeology); Relevance (law); Boosting (machine learning); Focus (optics); Topic model; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002081459,0.0001292029,0.0001612394,0.0004812973,0.0003818889,0.0001676761,0.0004021722,0.0001541535,0.000008060339],"category_scores_gemma":[0.0000448333,0.00009871394,0.0000296104,0.0004962771,0.000155426,0.0008524552,0.00001632753,0.0001553226,0.0000425171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003964533,"about_ca_system_score_gemma":0.00005964601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004510248,"about_ca_topic_score_gemma":0.0000167966,"domain_scores_codex":[0.9990462,0.00001359627,0.0003367077,0.0001686128,0.0001932554,0.0002416284],"domain_scores_gemma":[0.9988706,0.00005204876,0.00006981091,0.0004245617,0.0005260875,0.00005688642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000656679,0.0001510594,0.0001091303,0.0002096182,0.0001555323,0.000003581698,0.004187599,0.003057137,0.000501046,0.5708863,0.00008696261,0.4199954],"study_design_scores_gemma":[0.001405903,0.005084957,0.00001714925,0.0001894709,0.00003996369,0.0003178277,0.005147909,0.8884999,0.05265683,0.003668477,0.0423512,0.0006203832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03498467,0.00002887579,0.9627803,0.0009221107,0.0003361298,0.000527308,0.00001665535,0.0002715231,0.000132447],"genre_scores_gemma":[0.9919842,0.00003846953,0.00768036,0.00007385021,0.00002129488,0.00007895032,0.000004295139,0.000005424587,0.0001131763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9569995,"threshold_uncertainty_score":0.4025437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02722558212257443,"score_gpt":0.2683283286988506,"score_spread":0.2411027465762762,"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."}}