{"id":"W4409795013","doi":"10.61091/jcmcc127b-421","title":"Tucker inference learning method for knowledge graph decision making","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science Foundation of China University of Petroleum, Beijing; China University of Petroleum, Beijing","keywords":"Inference; Computer science; Graph; Artificial intelligence; Machine learning; Theoretical computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00221429,0.0002706975,0.0008239385,0.0004107963,0.0003183377,0.0001534892,0.000372371,0.000387498,0.000002164041],"category_scores_gemma":[0.00157015,0.0002506556,0.0002114453,0.0004488979,0.00004430664,0.0001594869,0.000120531,0.0007811527,0.000001153967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009732337,"about_ca_system_score_gemma":0.00009054932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001465525,"about_ca_topic_score_gemma":4.159875e-7,"domain_scores_codex":[0.9980735,0.00009243409,0.001109896,0.0001663332,0.0002253511,0.0003325166],"domain_scores_gemma":[0.995625,0.003232382,0.0004318929,0.0001692727,0.0004725454,0.00006886642],"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":[0.0001286108,0.0001307525,0.0001052836,0.0003494889,0.0002556041,0.000004786126,0.0005231923,0.001919185,0.0006758903,0.9411107,0.0004557128,0.05434081],"study_design_scores_gemma":[0.004792653,0.0003640784,0.00002288211,0.001530577,0.0001594956,0.00002746613,0.0003609343,0.139948,0.0006349799,0.8472315,0.004650138,0.0002772894],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1240133,0.001381974,0.8425028,0.00004410118,0.02899503,0.0004720898,0.000001273519,0.0001789874,0.002410408],"genre_scores_gemma":[0.9793133,0.0000283168,0.01972024,0.000007279725,0.0008822031,0.000006942489,4.880158e-7,0.00003048913,0.00001070648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8553,"threshold_uncertainty_score":0.9999946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01518226463890753,"score_gpt":0.2992167684957241,"score_spread":0.2840345038568166,"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."}}