{"id":"W7062477743","doi":"","title":"What do we mean when we say semantic? A multidisciplinary semantic glossary","year":2024,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Deafness and Other Communication Disorders; National Institute on Aging; Biotechnology and Biological Sciences Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; National Natural Science Foundation of China; Max-Planck-Gesellschaft; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; Economic and Social Research Council; European Commission; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; National Institute for Health and Care Research; National Institutes of Health; National Science Foundation","keywords":"Ambiguity; Glossary; Meaning (existential); Perspective (graphical); Multidisciplinary approach; Cognition; Embodied cognition; Semantics (computer science); Semantic memory; Semantic analysis (machine learning)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009616149,0.001504057,0.00148184,0.0111289,0.006033659,0.01342412,0.00213844,0.003243997,0.006684754],"category_scores_gemma":[0.02407985,0.000592051,0.0006692902,0.008576695,0.01648569,0.01744311,0.00447427,0.008762709,0.002658496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006454961,"about_ca_system_score_gemma":0.002998534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007759803,"about_ca_topic_score_gemma":0.005979131,"domain_scores_codex":[0.991507,0.004067392,0.001753814,0.000967715,0.001380496,0.0003235687],"domain_scores_gemma":[0.9863058,0.007769908,0.001110501,0.001589408,0.002660643,0.0005637232],"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":[0.00003091112,0.000008176334,0.0002761357,0.0002792647,0.00001147039,0.00006399664,0.008803337,0.0001224345,0.000498583,0.9121732,0.05354288,0.02418959],"study_design_scores_gemma":[0.000008759345,0.00003127018,0.001503696,0.001753379,0.0000262141,0.0004706581,0.0132898,0.001032959,0.0005853447,0.3955892,0.5856494,0.00005933569],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02164079,0.1011048,0.2663726,0.2468662,0.01888941,0.0007165945,0.006440945,0.001020251,0.3369484],"genre_scores_gemma":[0.5897862,0.06182548,0.2444902,0.04203043,0.01306867,0.002697504,0.009751901,0.001455466,0.03489422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01342412,"threshold_uncertainty_score":0.0508557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009058221767438944,"score_gpt":0.2079230987848423,"score_spread":0.1988648770174034,"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."}}