{"id":"W2404523947","doi":"","title":"Towards Semantic Integration of Legacy Databases for Homeland Security.","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Metadata; Ontology; World Wide Web; Context (archaeology); Vocabulary; Database; Homeland security; Semantic Web; Information retrieval","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01507327,0.0007111798,0.001001099,0.00446303,0.002130929,0.009779511,0.003209421,0.002279071,0.001785159],"category_scores_gemma":[0.01440012,0.0007743864,0.001747108,0.005206379,0.002054785,0.02253474,0.01106464,0.003626741,0.001145965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002226518,"about_ca_system_score_gemma":0.004624575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005566659,"about_ca_topic_score_gemma":0.007966769,"domain_scores_codex":[0.9934323,0.002330484,0.001000256,0.0006212608,0.002319378,0.0002963296],"domain_scores_gemma":[0.9923189,0.00132267,0.0005713058,0.002607517,0.002675761,0.0005037527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000128924,0.0002465967,0.003620185,0.0007530933,0.0002775254,0.0011742,0.004673698,0.005192658,0.005472925,0.6717833,0.03455576,0.2721213],"study_design_scores_gemma":[0.00004029521,0.00006247036,0.002068173,0.001469379,0.0002953567,0.001083957,0.004904017,0.06856541,0.006821002,0.3878753,0.5267245,0.00009016535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008768144,0.003813542,0.9610864,0.005240696,0.0005304632,0.0003430019,0.000650801,0.002666577,0.01690039],"genre_scores_gemma":[0.07774372,0.003792178,0.9066866,0.001705932,0.0002083632,0.0002466402,0.005046615,0.0004033622,0.004166621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01507327,"threshold_uncertainty_score":0.07971603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1757340789809956,"score_gpt":0.3787654694567645,"score_spread":0.2030313904757689,"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."}}