{"id":"W2026485377","doi":"10.1007/s007780200061","title":"Locating and accessing data repositories with WebSemantics","year":2002,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Metadata; Computer science; World Wide Web; Data element; Interoperability; Data discovery; Data sharing; Metadata repository; Data mapping; Linked data; Semantics (computer science); Data publishing; Information retrieval; Data science; Semantic Web; Publishing; Database","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":[0.0003730603,0.00007438666,0.0000884909,0.00002390027,0.0006901779,0.000463257,0.0006702052,0.00001240363,0.000003583823],"category_scores_gemma":[0.00004841847,0.00003873181,0.000007204468,0.0001425472,0.00008430439,0.001712644,0.0004737065,0.0001799504,0.000002813921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007994224,"about_ca_system_score_gemma":0.00001830082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000955006,"about_ca_topic_score_gemma":0.000007925438,"domain_scores_codex":[0.9992916,0.00005041151,0.000159765,0.0001513497,0.0001951514,0.0001517028],"domain_scores_gemma":[0.998964,0.0000666205,0.000164708,0.0006997419,0.00005243592,0.00005254174],"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":[0.00005055993,0.0001631959,0.03028619,0.0002590527,0.0002965659,0.001387103,0.02495763,0.001100237,0.004917883,0.1914974,0.02087028,0.7242139],"study_design_scores_gemma":[0.001969845,0.000507936,0.007333971,0.001615975,0.0001361502,0.05863261,0.005154338,0.5959258,0.004408337,0.00427085,0.3186253,0.001418902],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01906438,0.002125532,0.9760068,0.001853383,0.0003289663,0.00003890696,0.000002480309,0.0000357233,0.0005438156],"genre_scores_gemma":[0.7713348,0.0002021106,0.2273845,0.0001425308,0.0006319269,8.536715e-7,0.000001023306,0.000009736003,0.0002925005],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7522705,"threshold_uncertainty_score":0.530836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05989600271466623,"score_gpt":0.2653695745818546,"score_spread":0.2054735718671883,"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."}}