{"id":"W4393284893","doi":"10.23977/jaip.2024.070117","title":"Optimization and Application of Natural Language Processing Models Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Natural (archaeology); Deep learning; Artificial intelligence; Natural language processing; History; Archaeology","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":[0.0007529507,0.00006026484,0.00008718258,0.0002213496,0.00007292975,0.00010513,0.0002110842,0.0000553616,0.000004550713],"category_scores_gemma":[0.0005567971,0.00005237236,0.00002826807,0.0003618534,0.00004835084,0.001238228,0.00002279964,0.0003894482,0.000003528805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002578298,"about_ca_system_score_gemma":0.0001268343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008014255,"about_ca_topic_score_gemma":0.000001598487,"domain_scores_codex":[0.9992074,0.00007931627,0.000308261,0.0001267843,0.0002022251,0.00007600094],"domain_scores_gemma":[0.9986648,0.0006213856,0.0003022854,0.00009047236,0.0002938544,0.00002719883],"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":[0.00002748214,0.0000508769,0.000004644513,0.00002142581,0.000006504347,0.00000600736,0.001608825,0.4851842,0.0007586358,0.0346295,0.000002322303,0.4776996],"study_design_scores_gemma":[0.000009261697,0.0001171484,0.000005651284,0.00006000466,0.00001549665,0.00007369695,0.001173681,0.9858084,0.00588042,0.006632125,0.0001750783,0.00004900233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002182506,0.002544399,0.9905691,0.004126942,0.0002131243,0.00004687729,1.411279e-7,0.00002999477,0.0002869371],"genre_scores_gemma":[0.8697875,0.00006963879,0.1299349,0.000118157,0.0000732629,0.000001647704,5.792488e-7,0.000004018208,0.00001030779],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.867605,"threshold_uncertainty_score":0.2135683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03176149367351076,"score_gpt":0.3681730571005958,"score_spread":0.336411563427085,"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."}}