{"id":"W4293212745","doi":"10.1007/978-3-031-02181-7_4","title":"Refining Query and Document Representations","year":2022,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on human language technologies","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Computer science; Vocabulary; Matching (statistics); Information retrieval; Section (typography); Artificial intelligence; Core (optical fiber); Natural language processing; Semantic matching; Linguistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002287575,0.0004532865,0.0005165475,0.0006945865,0.0005320919,0.0002433796,0.001583321,0.0003784497,0.0003587113],"category_scores_gemma":[0.0005425424,0.0003868887,0.0001508574,0.00008609962,0.0003154629,0.0001602155,0.001267667,0.0007549564,0.00002905743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258167,"about_ca_system_score_gemma":0.00003839736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228991,"about_ca_topic_score_gemma":0.0001617016,"domain_scores_codex":[0.9978645,0.00004905906,0.0003454121,0.00094127,0.0004448384,0.0003549359],"domain_scores_gemma":[0.9972067,0.000785753,0.0002795758,0.00167068,0.00002603773,0.00003119686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000005059077,0.000009832774,0.00001330947,0.00003683343,0.0001010647,0.0002772889,0.0006047718,0.00001089128,0.0003489405,0.8719395,0.002434257,0.1242182],"study_design_scores_gemma":[0.0004495384,0.0007425433,0.0005793533,0.0007120956,0.000251913,0.000255221,0.003721247,0.00003524169,0.02855086,0.7951288,0.166864,0.002709164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002732393,0.02596311,0.001811682,0.007034308,0.0005577352,0.0006745829,0.00004864506,0.01142944,0.9497481],"genre_scores_gemma":[0.7447021,0.001672533,0.03129561,0.001118241,0.0002041089,0.0007757049,0.00004286603,0.0002354499,0.2199534],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7419698,"threshold_uncertainty_score":0.9998583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776359311982873,"score_gpt":0.290113440396588,"score_spread":0.2623498472767593,"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."}}