{"id":"W2946940318","doi":"","title":"UWaterlooMDS at the TREC 2018 Common Core Track.","year":2018,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Track (disk drive); Computer science; Core (optical fiber); Information retrieval; Artificial intelligence; Telecommunications; Operating system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002314279,0.001460442,0.002659148,0.004353568,0.002615551,0.006280616,0.002161644,0.001624942,0.7170138],"category_scores_gemma":[0.005034408,0.0007373211,0.0006103997,0.006676141,0.0006332745,0.00531549,0.003123145,0.001950382,0.5120502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003598168,"about_ca_system_score_gemma":0.00492426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07756723,"about_ca_topic_score_gemma":0.2305425,"domain_scores_codex":[0.9985167,0.0001796031,0.00007155792,0.0002897819,0.0007339402,0.0002084211],"domain_scores_gemma":[0.9953995,0.0003472846,0.0001054306,0.0004909902,0.002269926,0.001386722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000225653,0.00002420315,0.00004463292,0.0000343797,0.000001959254,0.000008275456,0.000008047572,0.00002680463,0.0001687156,0.0003989428,0.9888188,0.01044266],"study_design_scores_gemma":[0.00002923078,0.0000182904,0.0007514722,0.00005282773,0.000004314703,0.00001615334,0.00006021081,0.0003416156,0.0003624454,0.001705245,0.9966434,0.00001471628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0024558,0.004608874,0.007300077,0.0155866,0.01267632,0.0007554541,0.2860174,0.0164507,0.6541489],"genre_scores_gemma":[0.00412772,0.001656484,0.004688639,0.001521651,0.001274119,0.0001773921,0.108339,0.002701893,0.8755131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7170138,"threshold_uncertainty_score":0.4036454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03971195414912118,"score_gpt":0.2932357582956169,"score_spread":0.2535238041464958,"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."}}