{"id":"W2586297563","doi":"10.29173/cais15","title":"Creating a Metadata-Enabled Framework for Resource Discovery in Knowledge Bases","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Metadata; Computer science; World Wide Web; Data element; Geospatial metadata; Namespace; Intranet; Information retrieval; Ontology; Metadata repository; Interoperability; Meta Data Services; RDF; The Internet; Data science; Semantic Web; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0007437851,0.000268452,0.0005902502,0.0001989834,0.0001341946,0.00515932,0.003158578,0.0001611302,0.000007541763],"category_scores_gemma":[0.03449683,0.0001919184,0.0001849962,0.0005876732,0.0002638143,0.02114617,0.001112402,0.0002603194,0.000002498562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004385722,"about_ca_system_score_gemma":0.0002269218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004380259,"about_ca_topic_score_gemma":0.00002961332,"domain_scores_codex":[0.9981141,0.00003480709,0.000574352,0.0004524972,0.0003034286,0.0005207778],"domain_scores_gemma":[0.9813462,0.00136752,0.0007473819,0.0004546891,0.01597663,0.0001075982],"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":[0.0001474738,0.0004811089,0.1575004,0.001053825,0.0001699994,0.000001856852,0.05721287,0.0000172656,0.01157814,0.7448962,0.007124322,0.01981655],"study_design_scores_gemma":[0.002769391,0.001217062,0.3400427,0.003813636,0.0002330784,0.00006214854,0.02319169,0.02263025,0.1640374,0.3742004,0.06606607,0.001736144],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749569,0.0003917871,0.014035,0.00276078,0.000151146,0.0007629765,0.00004263578,0.00008134727,0.006817407],"genre_scores_gemma":[0.9729319,0.00002985517,0.025821,0.0002171834,0.0000703254,0.0001291491,0.000001782604,0.00001558063,0.0007832141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3706958,"threshold_uncertainty_score":0.9958734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129742145792862,"score_gpt":0.2684467891878737,"score_spread":0.237149367729945,"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."}}