{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0111266,0.003237561,0.002297489,0.004455529,0.003778569,0.003299525,0.003895885,0.003002306,0.0217208],"category_scores_gemma":[0.02278247,0.0009350904,0.001688644,0.003494487,0.001051601,0.005731988,0.005957284,0.004136692,0.01687005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002773952,"about_ca_system_score_gemma":0.008280939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07219913,"about_ca_topic_score_gemma":0.1530302,"domain_scores_codex":[0.9935637,0.002311767,0.0003134765,0.00131819,0.001608227,0.0008846376],"domain_scores_gemma":[0.9891642,0.002277741,0.0002475764,0.002822126,0.004147489,0.001340788],"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":[0.001477373,0.0007355039,0.002120504,0.0008959327,0.0003242542,0.0001077829,0.0003069178,0.003047251,0.01027969,0.0008920518,0.8659981,0.1138145],"study_design_scores_gemma":[0.006119966,0.0032509,0.04105708,0.0005776383,0.001359933,0.0006725965,0.002023429,0.1625678,0.0746528,0.01374662,0.6931241,0.00084707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1594434,0.01895883,0.1607955,0.008221842,0.01909865,0.009272477,0.4174076,0.1449847,0.06181698],"genre_scores_gemma":[0.1284813,0.001141093,0.1218232,0.001577698,0.001724815,0.00323184,0.6818519,0.005385321,0.05478282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07219913,"threshold_uncertainty_score":0.1435578,"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."}}