{"id":"W1539683106","doi":"10.21236/ada458694","title":"Construction of Chinese-English Semantic Hierarchy for Information Retrieval","year":2000,"lang":"en","type":"report","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; U.S. Department of Defense","keywords":"Hierarchy; Computer science; Information retrieval; Natural language processing; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006345831,0.0002290387,0.0004065193,0.0004951805,0.00006795485,0.0001784657,0.0007955208,0.0003694309,0.00002460227],"category_scores_gemma":[0.0009646338,0.0001796432,0.0001590746,0.0005998633,0.00007245292,0.0015179,0.0001382602,0.0003059254,0.00000294093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012673,"about_ca_system_score_gemma":0.0008030339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005473396,"about_ca_topic_score_gemma":0.00000266693,"domain_scores_codex":[0.998096,0.00002347826,0.0006533063,0.0002562954,0.0007682376,0.0002026507],"domain_scores_gemma":[0.9970425,0.0001048617,0.0005748814,0.0005308872,0.001701639,0.00004520069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001326402,0.00006968751,0.0004400952,0.004950455,0.0001574326,0.000006138843,0.002448211,0.000007062992,0.0001945338,0.04757881,0.03699796,0.907017],"study_design_scores_gemma":[0.001841102,0.0009924647,0.0003905207,0.002042206,0.0001830032,0.0008391541,0.00008460489,0.01738446,0.04012648,0.4333951,0.4998139,0.002907068],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005835806,0.001213881,0.9393094,0.000114698,0.001974285,0.000885122,0.00005161449,0.001382681,0.05448477],"genre_scores_gemma":[0.02309657,0.0003971538,0.9744799,0.0000814758,0.0004276403,0.00002337674,0.0002136705,0.00001926442,0.001260983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9041099,"threshold_uncertainty_score":0.7325639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065408082120032,"score_gpt":0.2821486499774501,"score_spread":0.2714945691562498,"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."}}