{"id":"W2121587139","doi":"","title":"York University at TREC 2009: Relevance Feedback Track","year":2009,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Relevance feedback; Relevance (law); Computer science; Weighting; Task (project management); Track (disk drive); Domain (mathematical analysis); Information retrieval; Series (stratigraphy); Artificial intelligence; Mathematics; Engineering; Image retrieval","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":[0.0189547,0.002167927,0.00240281,0.003347807,0.004405282,0.002766868,0.003120235,0.002573419,0.02452498],"category_scores_gemma":[0.02654865,0.001009685,0.00078451,0.002966581,0.0008509378,0.004310098,0.001853188,0.004431332,0.02435691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00442891,"about_ca_system_score_gemma":0.007254315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1147386,"about_ca_topic_score_gemma":0.1680787,"domain_scores_codex":[0.9909912,0.003162188,0.0005628322,0.001157213,0.003500853,0.0006257686],"domain_scores_gemma":[0.9733786,0.004607114,0.0006876253,0.003951746,0.01515901,0.002215877],"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":[0.0007287416,0.001454767,0.001447517,0.00045324,0.0001054951,0.00007205534,0.0001647705,0.002374944,0.008225492,0.0008841908,0.9236892,0.0603997],"study_design_scores_gemma":[0.004824075,0.004532778,0.04473497,0.0003018593,0.0003734645,0.0004969133,0.000539904,0.0928252,0.06401194,0.005096456,0.7814369,0.0008255605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2025152,0.01350917,0.1023214,0.01792419,0.01224547,0.02988365,0.3752046,0.1114701,0.1349262],"genre_scores_gemma":[0.1365526,0.001943112,0.1421335,0.003308128,0.0009143121,0.00908906,0.5800944,0.003392031,0.1225728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1147386,"threshold_uncertainty_score":0.2281416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03342038611951475,"score_gpt":0.2467342868726649,"score_spread":0.2133139007531501,"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."}}