{"id":"W2894485755","doi":"","title":"Overview of the TREC 2016 Contextual Suggestion Track","year":2017,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval; Pooling; World Wide Web; Context (archaeology); Track (disk drive); Matching (statistics); Point (geometry); Task (project management); Point of interest; Web page; NIST; Question answering; Natural language processing; Artificial intelligence","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":[0.01470257,0.002283048,0.002001086,0.01102562,0.003512592,0.004292252,0.003351172,0.002482696,0.0226052],"category_scores_gemma":[0.01845613,0.001446798,0.001633146,0.01116703,0.0008177641,0.005852601,0.002520039,0.002547634,0.02436069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005413699,"about_ca_system_score_gemma":0.01343943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1575812,"about_ca_topic_score_gemma":0.180019,"domain_scores_codex":[0.9897816,0.002233352,0.0008088895,0.001492995,0.004785837,0.0008972994],"domain_scores_gemma":[0.982397,0.002214462,0.0006047196,0.00234053,0.01087075,0.001572558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007019307,0.0004100664,0.002385506,0.001860154,0.00014937,0.0001082631,0.0004003251,0.003365658,0.01058469,0.001823825,0.7648526,0.2133576],"study_design_scores_gemma":[0.0002040701,0.0006421059,0.009097759,0.000409712,0.0001586637,0.0001932817,0.0002819752,0.00838126,0.01045156,0.001572506,0.9683651,0.0002419718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.05549725,0.07152458,0.1416913,0.01643435,0.009729593,0.0152591,0.4067651,0.1404479,0.1426509],"genre_scores_gemma":[0.05553618,0.01222947,0.1711988,0.002978484,0.001802309,0.005641453,0.6714705,0.005884292,0.0732586],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1575812,"threshold_uncertainty_score":0.313328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06058561015529065,"score_gpt":0.3034118240712401,"score_spread":0.2428262139159495,"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."}}