{"id":"W2354970203","doi":"","title":"Optimizing Persistence Objects Query of Jdo Based on Objects Access Layer","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Layer (electronics); Factor (programming language); Persistence (discontinuity); Object (grammar); Filter (signal processing); Java; Index (typography); Persistent data structure; Database; Operating system; Artificial intelligence; Programming language; Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002138568,0.0002430696,0.0002872054,0.0002226512,0.0002285139,0.0003232746,0.001913254,0.00009348811,0.000005067158],"category_scores_gemma":[0.000001114364,0.0002438004,0.0001677144,0.0008847875,0.00005774896,0.0002559263,0.0003070731,0.0001583186,0.00006501022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006808028,"about_ca_system_score_gemma":0.0001337277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001114851,"about_ca_topic_score_gemma":0.00001134272,"domain_scores_codex":[0.9981422,0.00007848446,0.0004540488,0.0006554404,0.0002941559,0.0003756042],"domain_scores_gemma":[0.9983824,0.0002029932,0.0002637442,0.0008732305,0.0001914151,0.0000861977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002146947,0.001565102,0.002223921,0.0004031766,0.00007718611,0.00002053436,0.001337959,0.8910919,0.02131507,0.03721302,0.01778066,0.02694999],"study_design_scores_gemma":[0.001401244,0.000176181,0.006049838,0.0003684915,0.00003390919,0.00004472284,0.00004913413,0.8611147,0.03376634,0.001838431,0.09406099,0.001096008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002956152,0.0001167764,0.9867163,0.0002661178,0.00007208477,0.0005734462,0.00001837763,0.0003051556,0.008975605],"genre_scores_gemma":[0.7900969,0.00000104218,0.2091153,0.0003609082,0.0001638307,0.0001101543,0.00003844243,0.00001399011,0.000099394],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7871408,"threshold_uncertainty_score":0.9941892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02303782451289658,"score_gpt":0.2541541814132001,"score_spread":0.2311163569003035,"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."}}