{"id":"W4220924437","doi":"10.1080/00207543.2022.2042416","title":"An ABGE-aided manufacturing knowledge graph construction approach for heterogeneous IIoT data integration","year":2022,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Knowledge graph; Computer science; Leverage (statistics); Knowledge integration; Graph; Embedding; Domain knowledge; Knowledge management; Data science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002133775,0.0001156755,0.0001354748,0.000914512,0.0002332325,0.0002161061,0.001127749,0.00005463043,0.0001127944],"category_scores_gemma":[0.0001398762,0.0001200121,0.00007158221,0.0002597375,0.00009149787,0.001553895,0.0001117483,0.000774291,0.000004066582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004317448,"about_ca_system_score_gemma":0.0001042884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004971345,"about_ca_topic_score_gemma":0.000002923365,"domain_scores_codex":[0.9978104,0.000150751,0.0005658153,0.0002366319,0.00102141,0.0002149971],"domain_scores_gemma":[0.9984325,0.00007199459,0.0001240611,0.0003406442,0.0009408651,0.00008989299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004135879,0.0004812817,0.00009499483,0.00009923008,0.0004110376,0.00001129232,0.0009981885,0.7409825,0.01514028,0.00141586,0.01264028,0.2273114],"study_design_scores_gemma":[0.003633942,0.001381078,0.00044077,0.0001700534,0.0000838534,0.007727131,0.01671458,0.3096805,0.4947649,0.01433928,0.1500583,0.001005613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6646699,0.0005272588,0.3062517,0.001294158,0.01612292,0.001353101,0.0005392093,0.0002751939,0.008966569],"genre_scores_gemma":[0.9899533,0.00005800328,0.008182727,0.000007400145,0.001227963,0.00006303061,0.0003525602,0.00003161314,0.0001233664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4796246,"threshold_uncertainty_score":0.4893953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1576773952486558,"score_gpt":0.3919691833917098,"score_spread":0.234291788143054,"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."}}