{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001070833,0.0004230057,0.0003930498,0.0006283514,0.0002989008,0.0003246477,0.0005936123,0.0002497178,0.0001901236],"category_scores_gemma":[0.00001187412,0.0004762303,0.0002361783,0.0009499657,0.0001788961,0.005123446,0.0005010377,0.000457362,0.0004855066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004275079,"about_ca_system_score_gemma":0.00008751119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001739566,"about_ca_topic_score_gemma":0.0002144857,"domain_scores_codex":[0.9982104,0.00005070357,0.0002375063,0.000626781,0.0003474805,0.000527124],"domain_scores_gemma":[0.9989815,0.0001331082,0.00004244683,0.0006798709,0.0000454217,0.0001176342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006629545,0.0006743549,0.001133646,0.006147765,0.002917016,0.01727315,0.03570598,0.1297824,0.04935668,0.297673,0.3878814,0.07079162],"study_design_scores_gemma":[0.003331461,0.0003950506,0.0006816395,0.004662125,0.0006286828,0.0003431998,0.08796764,0.4516429,0.01642055,0.04194091,0.3881444,0.003841424],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7204205,0.06671486,0.1671168,0.01927034,0.009089845,0.001815374,0.001522951,0.009174287,0.004874963],"genre_scores_gemma":[0.9340375,0.02065427,0.003739695,0.00003692325,0.0001445186,0.000004662124,0.00005313871,0.0001091457,0.04122014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3218604,"threshold_uncertainty_score":0.9997689,"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."}}