{"id":"W2395887233","doi":"","title":"University of Waterloo at TREC 2015 Microblog Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Microblogging; Exploit; Social media; Information retrieval; Relevance (law); Word (group theory); Query expansion; World Wide Web; Mathematics","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":[0.005637045,0.00251254,0.002271261,0.005779509,0.004492575,0.005784139,0.002549804,0.001869338,0.0838113],"category_scores_gemma":[0.01237456,0.001227766,0.00071723,0.005595828,0.001108484,0.006609804,0.002053061,0.002493931,0.05841029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01115282,"about_ca_system_score_gemma":0.01405893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4736371,"about_ca_topic_score_gemma":0.6227235,"domain_scores_codex":[0.9957131,0.0008190097,0.0002593474,0.0009144402,0.001798771,0.0004953261],"domain_scores_gemma":[0.9900215,0.001518137,0.0003365645,0.001449693,0.005340749,0.00133327],"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":[0.0000712699,0.00008953497,0.0004119163,0.0002512846,0.00002077151,0.00002778485,0.00006082977,0.000390792,0.0006510253,0.0007795286,0.976629,0.02061644],"study_design_scores_gemma":[0.0003085034,0.0001435917,0.007259942,0.0002870965,0.00005167482,0.00007500294,0.0003645313,0.01617714,0.00377474,0.003437496,0.9680072,0.000113003],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01467896,0.009080539,0.01293859,0.0117364,0.002935725,0.002457402,0.7627143,0.02892181,0.1545363],"genre_scores_gemma":[0.02601984,0.002701474,0.02079433,0.001086059,0.0003996948,0.0009350247,0.8446509,0.00162089,0.1017917],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4736371,"threshold_uncertainty_score":0.9417605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0386870167005127,"score_gpt":0.2642413455623248,"score_spread":0.2255543288618121,"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."}}