{"id":"W3197771275","doi":"10.3390/app11178072","title":"Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Power Generation; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Knowledge base; Domain knowledge; Task (project management); Semantic network; Set (abstract data type); Domain (mathematical analysis); Entity linking; Key (lock); Information retrieval; Knowledge management; Artificial intelligence; Engineering; Programming language; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006260149,0.000552806,0.000465847,0.004367737,0.00093646,0.001659618,0.001354809,0.0007110552,0.004091148],"category_scores_gemma":[0.003336124,0.0003820613,0.0007466798,0.00292718,0.0004849425,0.004298661,0.001427222,0.0007822359,0.001134553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001410089,"about_ca_system_score_gemma":0.002264966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02184678,"about_ca_topic_score_gemma":0.02453073,"domain_scores_codex":[0.9994258,0.00007436952,0.0000663025,0.0002225447,0.0001677161,0.00004319796],"domain_scores_gemma":[0.9992172,0.0002542081,0.00006871127,0.0001252494,0.000286098,0.00004857123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00031091,0.0004889785,0.01083357,0.0008256984,0.0002343039,0.002179241,0.002559043,0.08156376,0.01036384,0.05002717,0.0167634,0.8238501],"study_design_scores_gemma":[0.00006831014,0.0001514225,0.009070736,0.0004508929,0.0004228605,0.0009824729,0.002050983,0.7982374,0.02379963,0.06950143,0.0951605,0.0001033374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05866536,0.0006501167,0.9102801,0.0007996316,0.0001005687,0.0008406651,0.006408955,0.004229138,0.0180255],"genre_scores_gemma":[0.3893913,0.001352437,0.5863268,0.0002167912,0.00004410518,0.0008609436,0.01331242,0.0001533556,0.008341867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02184678,"threshold_uncertainty_score":0.04343921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06894122035937689,"score_gpt":0.3209201123230744,"score_spread":0.2519788919636975,"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."}}