{"id":"W4402402630","doi":"10.1109/ialp63756.2024.10661174","title":"The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning","year":2024,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Composition (language); Computer science; Power (physics); Mathematics education; Psychology; Linguistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005387518,0.0001365871,0.0001453736,0.00004738913,0.0002597114,0.0002010929,0.0005447412,0.00003055084,0.00002590182],"category_scores_gemma":[0.00005737789,0.00009433817,0.00009024315,0.0002052645,0.0000279456,0.001040692,0.0002444464,0.0001346606,0.00000347587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006172842,"about_ca_system_score_gemma":0.00008721341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004129736,"about_ca_topic_score_gemma":0.0000144152,"domain_scores_codex":[0.9986933,0.00003637898,0.0003751737,0.0003558787,0.0002517578,0.00028751],"domain_scores_gemma":[0.9990832,0.0003127971,0.00006380406,0.0004282281,0.00006394142,0.00004807176],"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":[0.0001435412,0.000161822,0.0007934929,0.0006631706,0.0004173334,0.00003107923,0.008770645,0.1656349,0.1750391,0.5752977,0.02821079,0.04483641],"study_design_scores_gemma":[0.0002925165,0.00002505382,0.00001166625,0.000106371,0.00000901273,0.000005816516,0.0002455509,0.9879299,0.0008686369,0.00968419,0.0007024309,0.0001188663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02914703,0.001236778,0.9671493,0.001397702,0.0002155216,0.000283849,0.0000919562,0.0001713135,0.0003065417],"genre_scores_gemma":[0.5173982,0.00001732976,0.4819597,0.0003903557,0.0000534012,0.00002727128,0.00008870978,0.00001093452,0.00005405654],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.822295,"threshold_uncertainty_score":0.3846999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661174862752009,"score_gpt":0.3005383846981572,"score_spread":0.2839266360706372,"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."}}