{"id":"W4395685550","doi":"10.2316/j.2024.206-1100","title":"CHINESE VOCATIONAL SKILLS EDUCATION QUALITY ASSESSMENT USING ATTENTIVE DUAL RESIDUAL GENERATIVE ADVERSARIAL NETWORK OPTIMISED WITH GAZELLE OPTIMISATION ALGORITHM, 1-10.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Vocational education; Residual; Adversarial system; Generative grammar; Dual (grammatical number); Quality (philosophy); Artificial intelligence; Generative adversarial network; Computer science; Machine learning; Algorithm; Psychology; Deep learning; Pedagogy; Epistemology; Philosophy","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":[0.0006858223,0.0005768184,0.0004127316,0.0008292643,0.0002814155,0.0005852557,0.0006475183,0.0006382318,0.003766435],"category_scores_gemma":[0.001412815,0.0001596348,0.0004798396,0.0003923366,0.0003313996,0.0004967476,0.0008640581,0.0006366218,0.0006957044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007630661,"about_ca_system_score_gemma":0.0008220875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401324,"about_ca_topic_score_gemma":0.0159589,"domain_scores_codex":[0.9997517,0.00005625271,0.00001078078,0.00007263685,0.00005931155,0.00004928744],"domain_scores_gemma":[0.9996353,0.0001475633,0.0000308449,0.00002773804,0.0001141343,0.00004432784],"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":[0.00034228,0.000160669,0.0159976,0.0001387997,0.0001083149,0.000115409,0.0001253535,0.59304,0.007806805,0.00294871,0.00718172,0.3720343],"study_design_scores_gemma":[0.000007115779,0.00005265989,0.00489891,0.00001114296,0.00001584288,0.00001640964,0.00003372007,0.9914228,0.001792834,0.001262123,0.0004769769,0.000009531458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2608446,0.001131728,0.7229949,0.0009009059,0.0001920458,0.0001274435,0.0006025945,0.001347219,0.01185849],"genre_scores_gemma":[0.9473024,0.0002113637,0.04187711,0.0001189442,0.00004037165,0.00006396168,0.0007251833,0.00007068829,0.009590055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01401324,"threshold_uncertainty_score":0.02786332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056040255674622,"score_gpt":0.3071445636983304,"score_spread":0.2965841611415842,"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."}}