{"id":"W4409603721","doi":"10.61091/jcmcc127b-185","title":"An Efficient Construction Method for Matrix Decomposition-Based Natural Language Processing Models in Low-Dimensional Embedding Space","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hanshan Normal University","keywords":"Embedding; Computer science; Space (punctuation); Decomposition; Matrix (chemical analysis); Matrix decomposition; Natural (archaeology); Parallel computing; Artificial intelligence; Materials science; Physics; Geography","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":[0.001128784,0.0002163932,0.0004640842,0.0003881614,0.0003132096,0.0002727638,0.0004914008,0.0001038284,3.191148e-7],"category_scores_gemma":[0.00009507241,0.0002085175,0.0001207633,0.0005604418,0.00004479667,0.0003480314,0.0001187404,0.0003197554,1.060374e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001560819,"about_ca_system_score_gemma":0.0002902058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004524291,"about_ca_topic_score_gemma":1.975688e-7,"domain_scores_codex":[0.998131,0.00008961358,0.0008453771,0.000297433,0.0003807451,0.0002558548],"domain_scores_gemma":[0.9975459,0.0008340658,0.0006971352,0.0002132275,0.0006165654,0.00009310465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004366882,0.0003388139,0.000005870744,0.0001371822,0.00001297414,0.00000375631,0.0003252845,0.1999055,0.001288383,0.7923531,0.000007732006,0.005577768],"study_design_scores_gemma":[0.001425981,0.0001026526,0.00000806292,0.0003715983,0.00001450833,0.00002145675,0.00008337844,0.6122629,0.001607312,0.3839717,0.000008523317,0.0001219188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09035725,0.0002635006,0.9065332,0.0003049998,0.002060301,0.0003752461,0.000001338085,0.00006620018,0.00003793304],"genre_scores_gemma":[0.5213462,7.863744e-7,0.4784899,0.00002943216,0.0001167327,0.000006409413,0.000001539621,0.000008206658,7.756539e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4309889,"threshold_uncertainty_score":0.8503098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007984324735971363,"score_gpt":0.3462701188903548,"score_spread":0.3382857941543834,"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."}}