{"id":"W4247644046","doi":"10.1007/978-1-4939-7131-2_100238","title":"Data Mash-Ups","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","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":[0.005609539,0.001014231,0.0009422173,0.003330937,0.00159344,0.005535842,0.002498183,0.001372867,0.06654629],"category_scores_gemma":[0.01723754,0.0007189579,0.001305072,0.004611281,0.001951351,0.01542659,0.006500075,0.004396861,0.02687331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001961377,"about_ca_system_score_gemma":0.001731132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001117982,"about_ca_topic_score_gemma":0.001458921,"domain_scores_codex":[0.9961374,0.0007524558,0.0002688078,0.0006236573,0.002052844,0.0001647316],"domain_scores_gemma":[0.992331,0.003430531,0.0002322655,0.002427319,0.001284777,0.0002940835],"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":[0.00003744732,0.0000238002,0.000193597,0.0003420566,0.00002090331,0.00009412084,0.000457108,0.001045721,0.0007520033,0.4638545,0.1658061,0.3673728],"study_design_scores_gemma":[0.000005669448,0.000009672775,0.0001018524,0.0002539416,0.00001234496,0.0001580411,0.0001377119,0.002158713,0.001047953,0.1577702,0.8383223,0.00002145095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002953316,0.01738369,0.5586406,0.02115031,0.009787201,0.0004596933,0.002450227,0.008815864,0.3783592],"genre_scores_gemma":[0.04710728,0.01875156,0.3721141,0.007069507,0.006230924,0.0006389472,0.004855526,0.005630699,0.5376014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06654629,"threshold_uncertainty_score":0.2226195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6487458488491252,"score_gpt":0.4840009790531912,"score_spread":0.164744869795934,"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."}}