{"id":"W2947119606","doi":"","title":"H2oloo at TREC 2018: Cross-Collection Relevance Transfer for the Common Core Track.","year":2018,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Track (disk drive); Relevance (law); Core (optical fiber); Transfer (computing); Information retrieval; Telecommunications; Parallel computing","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":[0.0005948459,0.0002267526,0.0002507451,0.00005994202,0.0007856716,0.000299567,0.001325553,0.0001595362,0.0001496448],"category_scores_gemma":[0.000218157,0.0001705511,0.0001141398,0.000463716,0.0003398022,0.0004226141,0.0001552814,0.0002462612,0.0001338744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001482706,"about_ca_system_score_gemma":0.0001708086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000851788,"about_ca_topic_score_gemma":0.0003544992,"domain_scores_codex":[0.9980556,0.00005386719,0.0003892961,0.0006478283,0.0003872442,0.000466151],"domain_scores_gemma":[0.9978097,0.0006273597,0.00008472426,0.0009855228,0.0003945768,0.00009809475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00343036,0.0003777269,0.00985256,0.0002544604,0.00024143,0.00002430836,0.01849632,0.0004856175,0.06739286,0.3727541,0.01730189,0.5093883],"study_design_scores_gemma":[0.002352968,0.001209364,0.01461127,0.0001011078,0.00005458682,0.00007585607,0.00006099185,0.8161827,0.08906925,0.01970493,0.05576152,0.0008154226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.310167,0.0002006231,0.6844872,0.001213687,0.001124186,0.0005793963,0.00001070457,0.0002100102,0.002007231],"genre_scores_gemma":[0.9878695,0.00006650633,0.003730929,0.0004049721,0.0002845697,0.00003018064,0.00000317948,0.00001725627,0.007592927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8156971,"threshold_uncertainty_score":0.6954873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0819327481816418,"score_gpt":0.3220630675340519,"score_spread":0.2401303193524101,"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."}}