{"id":"W318551555","doi":"10.1007/978-3-662-43984-5_26","title":"A Hybrid Scale-Out Cloud-Based Data Service for Worldwide Sensors","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cloud computing; Scalability; Geospatial analysis; Computer science; Construct (python library); Search engine indexing; Distributed computing; Scale (ratio); Service (business); Architecture; Tree (set theory); Volume (thermodynamics); Data science; Database; Geography; Remote sensing; Computer network; World Wide Web; Cartography; Operating system","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":[0.0006560648,0.0007816901,0.001009059,0.0006865616,0.001133147,0.001676495,0.00293127,0.0009579253,0.007588774],"category_scores_gemma":[0.000991811,0.0003229444,0.0006302585,0.00173182,0.0004177188,0.00210543,0.002914571,0.0008438211,0.002180542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009855152,"about_ca_system_score_gemma":0.001323103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007239594,"about_ca_topic_score_gemma":0.007686854,"domain_scores_codex":[0.9992582,0.00007078499,0.00003913061,0.0001349628,0.0003100936,0.0001869233],"domain_scores_gemma":[0.9994096,0.00006262906,0.00003158204,0.000173679,0.000198538,0.00012403],"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":[0.002338726,0.001307225,0.006280489,0.0003706732,0.0002443225,0.00166623,0.0004696658,0.0654676,0.143913,0.02754806,0.1074551,0.642939],"study_design_scores_gemma":[0.0001682625,0.0003399719,0.002802534,0.00002938522,0.00007623198,0.0006697055,0.0003646,0.9235778,0.02205896,0.01177743,0.03804855,0.00008658643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1054591,0.001476697,0.8459711,0.00148184,0.0009642955,0.0007070439,0.001074917,0.01814328,0.02472159],"genre_scores_gemma":[0.8252311,0.000424354,0.1578882,0.0007525269,0.0002795743,0.0001642193,0.001370161,0.0005131137,0.01337679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007588774,"threshold_uncertainty_score":0.02538699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03197104118822253,"score_gpt":0.2619694287126282,"score_spread":0.2299983875244057,"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."}}