{"id":"W2554197048","doi":"","title":"WaterlooClarke: TREC 2015 Contextual Suggestion Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Track (disk drive); Point of interest; Point (geometry); Contextual design; Information retrieval; Human–computer interaction; Artificial intelligence; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0008646448,0.0002280595,0.0002765622,0.0001060322,0.0000815113,0.0003243521,0.001146289,0.0001588551,0.00008777693],"category_scores_gemma":[0.0003783838,0.0002035396,0.00006388876,0.0003322641,0.00008950586,0.0008026169,0.0002562047,0.0003100569,0.0006006799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183085,"about_ca_system_score_gemma":0.0004602204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000141778,"about_ca_topic_score_gemma":0.00002922337,"domain_scores_codex":[0.9977766,0.0001593522,0.0003772588,0.000609761,0.0006286856,0.0004483608],"domain_scores_gemma":[0.998103,0.0000927095,0.0001195263,0.0008241447,0.0005023387,0.000358281],"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":[0.0005309596,0.0003878621,0.003183195,0.000078828,0.00009355727,0.0002479967,0.0186998,0.0005844976,0.01126868,0.4987576,0.02214758,0.4440194],"study_design_scores_gemma":[0.006457575,0.001575253,0.006000344,0.0002473948,0.0000638961,0.0003344719,0.001055458,0.8202107,0.03397452,0.05601557,0.0716818,0.002383039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2253199,0.0003694523,0.7568414,0.003400022,0.001132023,0.0003260137,0.000006330747,0.0006162168,0.01198872],"genre_scores_gemma":[0.9862878,0.00001351633,0.009056007,0.0002374208,0.000139374,0.000004592266,0.000007504444,0.00001233064,0.004241493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8196262,"threshold_uncertainty_score":0.8300105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0765092666209366,"score_gpt":0.2924376573472113,"score_spread":0.2159283907262747,"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."}}