{"id":"W1860914937","doi":"10.1007/11610113_36","title":"The Adaptability of English Based Web Search Algorithms to Chinese Search Engines","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Search engine; Scope (computer science); Adaptability; Information retrieval; Semantic search; Search algorithm; Search analytics; Web search engine; Beam search; Web crawler; Exploratory search; Web search query; World Wide Web; Algorithm; Programming language","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.0034639,0.0004957304,0.0006142585,0.000884579,0.0003963505,0.0006214554,0.006322812,0.0002122466,0.00002154244],"category_scores_gemma":[0.0006372101,0.0003388845,0.000228254,0.001869755,0.0008407501,0.0004288611,0.002073937,0.0008991031,0.00002423574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002055932,"about_ca_system_score_gemma":0.001193242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008408995,"about_ca_topic_score_gemma":0.0002223533,"domain_scores_codex":[0.9950269,0.0001156605,0.0006414166,0.001574368,0.001820948,0.0008206978],"domain_scores_gemma":[0.9938773,0.001757895,0.0001421503,0.002741805,0.001197011,0.0002838219],"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":[0.000008931353,0.00004660785,0.0003817435,0.00003788703,0.00002038101,0.00001039471,0.001227869,0.1615026,0.0001186612,0.001059153,0.00004801037,0.8355378],"study_design_scores_gemma":[0.0001747111,0.0001436507,0.0004710419,0.0001524154,0.000006867242,0.000003244051,8.329969e-7,0.9919933,0.001027035,0.001171669,0.004440046,0.0004151648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001142939,0.0003181412,0.9944603,0.001829004,0.0009698689,0.0003111814,0.00003507882,0.0001196811,0.000813735],"genre_scores_gemma":[0.3855552,0.0000560603,0.6118749,0.0005352215,0.001488553,0.00001298032,0.00001173168,0.00003871982,0.0004266399],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8351226,"threshold_uncertainty_score":0.9999063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183141131814815,"score_gpt":0.2678530435157057,"score_spread":0.2495389303342242,"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."}}