{"id":"W4409576398","doi":"10.61091/jcmcc127a-141","title":"A Study on Modeling and Computing of Lexical Semantic Relationships in Japanese Language Based on Multidimensional Vector Space","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Space (punctuation); Semantic space; Vector space; Natural language processing; Artificial intelligence; Linguistics; Mathematics; Pure mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003333,0.0007670066,0.0007917967,0.001943442,0.0007244895,0.001782854,0.0008902157,0.0004861263,0.0008035701],"category_scores_gemma":[0.004345384,0.0003630226,0.001240604,0.003709665,0.0008573458,0.004904451,0.0006357685,0.0007205849,0.0002524969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119263,"about_ca_system_score_gemma":0.001239306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02036813,"about_ca_topic_score_gemma":0.01136533,"domain_scores_codex":[0.9986606,0.0004077195,0.0001292432,0.0004196421,0.0003007812,0.00008188491],"domain_scores_gemma":[0.9988968,0.0005091058,0.0001653841,0.00009180519,0.0002953678,0.00004151095],"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":[0.000230267,0.0002131859,0.04577777,0.0008109682,0.0004356493,0.0008052253,0.002441068,0.3955231,0.01532273,0.1589871,0.003531973,0.3759209],"study_design_scores_gemma":[0.000004533602,0.00004863541,0.003988656,0.00001984862,0.00005211647,0.0001233522,0.0001598119,0.9814082,0.001175085,0.01153576,0.00144742,0.00003669948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09317315,0.001471072,0.9027419,0.0002553003,0.00004511262,0.00005705038,0.0001221521,0.0002803447,0.001854],"genre_scores_gemma":[0.7204819,0.002379424,0.2741244,0.00008456849,0.00007665086,0.0001806712,0.000496776,0.00007253643,0.00210312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02036813,"threshold_uncertainty_score":0.04049915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520947189350418,"score_gpt":0.3118539033676451,"score_spread":0.2866444314741409,"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."}}