{"id":"W4393319378","doi":"10.21203/rs.3.rs-4169544/v1","title":"Integrating Quantum CI with Generative AI for Taiwanese/English Co-Learning: TAIDE-based Knowledge Graph Construction and Multimodal Data Transformation","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Science and Technology Council","keywords":"Generative grammar; Transformation (genetics); Graph; Computer science; Artificial intelligence; Natural language processing; Theoretical computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001355726,0.000386162,0.0005754256,0.001457077,0.0008099133,0.001642389,0.001281126,0.0006253926,0.004941051],"category_scores_gemma":[0.006257935,0.0003239898,0.0009782722,0.001838404,0.001021227,0.003302458,0.003050487,0.001658057,0.0008637762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194054,"about_ca_system_score_gemma":0.001816973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104961,"about_ca_topic_score_gemma":0.01565961,"domain_scores_codex":[0.9991925,0.0003454869,0.00004912297,0.0002299983,0.0001108401,0.00007196418],"domain_scores_gemma":[0.9970351,0.001742487,0.0000929165,0.000637768,0.0003559399,0.0001357118],"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":[0.000264833,0.0003938913,0.006157705,0.0003973548,0.0002430708,0.0003576021,0.002499142,0.08284217,0.01891979,0.1754611,0.006892459,0.7055708],"study_design_scores_gemma":[0.00001875492,0.0000472516,0.001702706,0.00003480757,0.00005607779,0.00007824507,0.0006000428,0.8390718,0.006696729,0.146783,0.004879337,0.00003133355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04772488,0.0002894885,0.9406952,0.0006951103,0.00005870449,0.0001181878,0.0003563362,0.00168227,0.008379691],"genre_scores_gemma":[0.7226864,0.000207651,0.2717862,0.0001605603,0.00003691212,0.0001672322,0.0008642228,0.0002809957,0.003809893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0104961,"threshold_uncertainty_score":0.02087003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08285924914743456,"score_gpt":0.3999924440936812,"score_spread":0.3171331949462466,"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."}}