{"id":"W2014470649","doi":"10.1109/cefc.2010.5481391","title":"The use of semantic networks to adapt a design prototype for electromagnetic device optimization","year":2010,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Adaptation (eye); Curse of dimensionality; Inference engine; Construct (python library); Inference; Artificial intelligence; Case-based reasoning; Knowledge base; Human–computer interaction; Machine learning; Programming language","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":[0.0002914304,0.00008236964,0.0001028879,0.00003605498,0.0001188285,0.0001574711,0.000561433,0.0000510073,0.000003985433],"category_scores_gemma":[0.0003046838,0.00005143025,0.00003340176,0.0002636318,0.00002926445,0.0001782955,0.00007468188,0.00006780143,0.000002614329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004551031,"about_ca_system_score_gemma":0.00005556006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002989404,"about_ca_topic_score_gemma":0.0002099466,"domain_scores_codex":[0.9992478,0.00004225066,0.000172792,0.0001895928,0.0001055074,0.0002420853],"domain_scores_gemma":[0.9985898,0.0007164582,0.00005782086,0.000432557,0.0001616997,0.00004171256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002351988,0.0001249267,0.0003290279,0.00004093876,0.00005042393,0.000002661209,0.0004033119,0.7372626,0.009909219,0.142718,0.009157459,0.09976619],"study_design_scores_gemma":[0.0001144787,0.0004984472,0.0005095573,0.000006011089,0.000006986263,0.000004687081,0.000005127635,0.993563,0.002583054,0.0005209572,0.002107853,0.00007986059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001816714,0.00002791547,0.9948557,0.001517669,0.0001971275,0.001420992,9.042743e-8,0.00008545449,0.00007830955],"genre_scores_gemma":[0.1950002,0.000008224843,0.8042969,0.0002742286,0.00002767099,0.0001728843,3.189427e-7,0.000005827957,0.0002137145],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2563004,"threshold_uncertainty_score":0.2097265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05333463801650343,"score_gpt":0.2614084799438534,"score_spread":0.2080738419273499,"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."}}