{"id":"W4292572073","doi":"10.48550/arxiv.1312.7373","title":"Extending Contexts with Ontologies for Multidimensional Data Quality\\n Assessment","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Datalog; Context (archaeology); Quality (philosophy); Data quality; Quality assessment; Information retrieval; Database; Data mining; Data science; Evaluation methods; Engineering","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":[0.01406584,0.001156835,0.0009774752,0.005694867,0.00206221,0.007767159,0.002546427,0.002129402,0.001696543],"category_scores_gemma":[0.0310705,0.001086775,0.003031143,0.005246327,0.004479815,0.01920878,0.01122624,0.003510971,0.0004012182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002039872,"about_ca_system_score_gemma":0.00254236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006726168,"about_ca_topic_score_gemma":0.007330106,"domain_scores_codex":[0.9798558,0.009067569,0.002816284,0.002924239,0.004528672,0.0008075449],"domain_scores_gemma":[0.9758031,0.01082637,0.002255008,0.007296462,0.002962334,0.0008567463],"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":[0.00007039957,0.00008453574,0.005058397,0.0004944556,0.0001482986,0.0006432584,0.00378306,0.01673149,0.002934851,0.871906,0.002620727,0.09552451],"study_design_scores_gemma":[0.00003211362,0.00005583628,0.001466545,0.0004725728,0.0001752127,0.0005874822,0.001490363,0.0949835,0.00548395,0.814389,0.08073599,0.0001275498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006706195,0.0007163596,0.9875508,0.0011546,0.00007236949,0.0001265267,0.0002102636,0.0005969487,0.002865913],"genre_scores_gemma":[0.1570985,0.001066216,0.8393316,0.0004852822,0.0001190842,0.0002421105,0.000414223,0.0001922286,0.001050743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01406584,"threshold_uncertainty_score":0.07438815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6327342795842864,"score_gpt":0.3946261101238581,"score_spread":0.2381081694604283,"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."}}