{"id":"W7074041183","doi":"","title":"What We Mean When We Say Semantic: Toward A Multidisciplinary Semantic Glossary","year":2024,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Theoretical and Computational Physics","field":"Physics and Astronomy","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)","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":[0.03063436,0.003073614,0.003049739,0.01958531,0.008330017,0.02079641,0.005159816,0.005779948,0.00552092],"category_scores_gemma":[0.03768536,0.001621296,0.001586291,0.01192606,0.03258165,0.04133565,0.009928175,0.019239,0.00313979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008730825,"about_ca_system_score_gemma":0.006262331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007818004,"about_ca_topic_score_gemma":0.005703249,"domain_scores_codex":[0.981195,0.009162289,0.00435052,0.00203859,0.002700273,0.00055329],"domain_scores_gemma":[0.9704136,0.01626088,0.002699678,0.003333529,0.005826394,0.001466061],"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":[0.00001938229,0.000009401993,0.0001639219,0.0002598762,0.00001568075,0.00006886092,0.009931475,0.0001730437,0.0003319079,0.9541485,0.02076849,0.01410948],"study_design_scores_gemma":[0.0000111456,0.00001962743,0.0005645326,0.001341769,0.0000253467,0.000256959,0.01041714,0.00120679,0.0002673329,0.6672199,0.3186134,0.00005593512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01044364,0.05320014,0.6080114,0.1896797,0.0147212,0.0009465906,0.003578694,0.001277262,0.1181413],"genre_scores_gemma":[0.2252708,0.03894391,0.6682512,0.0328292,0.01087803,0.0042938,0.006189785,0.001837882,0.01150543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03063436,"threshold_uncertainty_score":0.1620119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01355646069256035,"score_gpt":0.2593224221319707,"score_spread":0.2457659614394103,"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."}}