{"id":"W2952247164","doi":"10.48550/arxiv.1204.5093","title":"The Distributed Ontology Language (DOL): Ontology Integration and Interoperability Applied to Mathematical Formalization","year":2012,"lang":"en","type":"preprint","venue":"University of Birmingham Research Archive, E-prints Repository","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Ontology; Interoperability; Computer science; Ontology components; Upper ontology; Ontology-based data integration; Modular design; Process ontology; Annotation; Description logic; Information retrieval; Software engineering; Ontology alignment; World Wide Web; Programming language; Semantic Web; Artificial intelligence; Epistemology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00163163,0.0002363363,0.0004790697,0.0003140204,0.0006756482,0.0001474776,0.00204986,0.000249255,0.000009644043],"category_scores_gemma":[0.0004999412,0.0001968714,0.000107372,0.0001965703,0.001046293,0.000180028,0.005389023,0.0009347851,0.00002438542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133069,"about_ca_system_score_gemma":0.000240277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001206204,"about_ca_topic_score_gemma":0.001002108,"domain_scores_codex":[0.9968975,0.0008634185,0.0003862867,0.0007029767,0.0005342996,0.0006154956],"domain_scores_gemma":[0.9969025,0.001044962,0.0002140428,0.001283877,0.0003040195,0.0002505477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.002043128,0.001543719,0.02978854,0.002097949,0.0008322182,0.0003229906,0.1502315,0.0003576525,0.08358413,0.4607796,0.004171526,0.2642471],"study_design_scores_gemma":[0.00382476,0.001479205,0.5690005,0.002020545,0.0002913759,0.0005937227,0.05815445,0.1560675,0.05580466,0.1348996,0.01490944,0.002954233],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3769197,0.0001578091,0.6139398,0.000796895,0.0002886284,0.0008647268,0.00001512387,0.0001128047,0.006904518],"genre_scores_gemma":[0.9775572,0.000080366,0.02192901,0.0000122793,0.0000500261,0.000008603855,0.00002462215,0.000009615123,0.0003282452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6006375,"threshold_uncertainty_score":0.8028181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313042235638664,"score_gpt":0.291853856404888,"score_spread":0.2605496328410216,"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."}}