{"id":"W7083438366","doi":"10.23977/aetp.2025.090510","title":"Comparative Analysis of Prediction Algorithms for Surface Roughness in AI-oriented Courses","year":2025,"lang":"en","type":"article","venue":"Advances in Educational Technology and Psychology","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Surface roughness; Mean squared error; Robustness (evolution); Boosting (machine learning); Machining; Gradient boosting; Ensemble learning; Regression; Predictive modelling; Support vector machine","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":[0.0002328335,0.00008108826,0.0004131169,0.001764044,0.00003370616,0.000002814157,0.0001247389,0.0002377828,0.00003070933],"category_scores_gemma":[0.00008156132,0.00009512934,0.00003055477,0.002580428,0.0002988465,0.0001505018,0.00001974656,0.0001547012,0.000002077442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004701282,"about_ca_system_score_gemma":0.00001729271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001210594,"about_ca_topic_score_gemma":0.00009268132,"domain_scores_codex":[0.9989629,0.000008487661,0.0005425381,0.000344846,0.000008926587,0.0001323383],"domain_scores_gemma":[0.9994192,0.0001557416,0.000211436,0.0001536754,0.00005240635,0.000007501568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001822401,0.0001288876,0.4190141,0.000005964628,0.00005141555,4.469045e-8,0.00003242154,0.0002609535,0.00001089851,0.579735,0.00006761422,0.0006744832],"study_design_scores_gemma":[0.0004268064,0.0000497597,0.3256961,0.000009114357,0.00001137915,4.06575e-7,0.0001688269,0.002346236,0.00005708692,0.6600948,0.01107895,0.00006057064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9364072,0.007643884,0.03858209,0.009898953,0.0005563443,0.0002715165,0.0001969651,0.00002718418,0.006415846],"genre_scores_gemma":[0.9950064,0.000590112,0.00377924,0.0002774127,0.000007656862,0.000116151,0.00008399968,0.000002063429,0.0001369024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09331805,"threshold_uncertainty_score":0.3879262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03569290722039332,"score_gpt":0.3727284184616501,"score_spread":0.3370355112412568,"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."}}