{"id":"W1597495308","doi":"10.1007/978-3-540-72667-8_5","title":"Empowering Software Maintainers with Semantic Web Technologies","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software Engineering Research","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Software maintenance; Software engineering; Traceability; Source code; Static program analysis; Semantic Web; Ontology; Software; Semantic technology; Software development; World Wide Web; Information retrieval; Social Semantic Web; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001161446,0.000673169,0.0005593426,0.002257337,0.0002263779,0.00060095,0.005248758,0.0004547891,0.000009554484],"category_scores_gemma":[0.0006656481,0.000568336,0.00009934122,0.001600491,0.001131856,0.0005560783,0.002420427,0.001615666,0.00005124197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006016999,"about_ca_system_score_gemma":0.0007863723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001363059,"about_ca_topic_score_gemma":0.000072572,"domain_scores_codex":[0.994799,0.00001349599,0.0004291271,0.001802515,0.001679194,0.001276697],"domain_scores_gemma":[0.9957489,0.001478484,0.0001642819,0.00213554,0.0003026561,0.000170074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001291598,0.00002783142,0.001833308,0.0001655247,0.00003586393,0.00155072,0.000480122,0.068239,0.000131864,0.00740701,0.00003135339,0.9200845],"study_design_scores_gemma":[0.00198065,0.001986763,0.002085472,0.005977944,0.00003970645,0.002276099,0.000005174017,0.8329731,0.00972872,0.126443,0.009778365,0.006724964],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003932257,0.0005324653,0.9947611,0.0004814513,0.0008244896,0.0004217326,0.000002368992,0.002076025,0.0005071362],"genre_scores_gemma":[0.1985428,0.00004702599,0.8006669,0.000231399,0.0001566482,0.00001390284,0.000002386471,0.00007938123,0.000259563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9133595,"threshold_uncertainty_score":0.9996768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01569839359448943,"score_gpt":0.2682209223338169,"score_spread":0.2525225287393275,"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."}}