{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006914636,0.0001340741,0.0002056813,0.00006929338,0.0009406632,0.0001060833,0.0006855996,0.00003979292,0.000005517924],"category_scores_gemma":[0.0000320351,0.0001257105,0.00004772358,0.001174786,0.0002917407,0.0001458958,0.0004835898,0.00006970344,0.000003024511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003360572,"about_ca_system_score_gemma":0.0004619963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002105956,"about_ca_topic_score_gemma":0.00003231838,"domain_scores_codex":[0.998403,0.00003567447,0.0003069426,0.0005824165,0.0002364549,0.0004355068],"domain_scores_gemma":[0.9991282,0.0002314167,0.0001464986,0.000226247,0.0001909558,0.00007667109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001286975,0.0002992823,0.002493447,0.00008706671,0.00005334999,0.000009626512,0.0312415,0.006011716,0.09608637,0.3150712,0.0002705765,0.548363],"study_design_scores_gemma":[0.001420717,0.0001556831,0.01096104,0.0006358798,0.0000308749,0.00004534354,0.003913486,0.4956725,0.4489933,0.0335674,0.003251214,0.001352585],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5368786,0.0001618741,0.4601479,0.00001082163,0.0002597455,0.0001204693,3.831355e-7,0.00003739674,0.002382821],"genre_scores_gemma":[0.6754879,8.35817e-7,0.324351,0.00004187195,0.00008129304,0.00001550699,8.58859e-7,0.000003354708,0.00001735638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5470104,"threshold_uncertainty_score":0.7234915,"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."}}