{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001957644,0.0007199434,0.0006949783,0.0008615275,0.000352802,0.001386325,0.01376913,0.0001753421,0.0000165789],"category_scores_gemma":[0.000122045,0.0006726858,0.0001379271,0.0005519401,0.0004143075,0.001048321,0.005310493,0.0005862159,0.0001454646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001595217,"about_ca_system_score_gemma":0.0004304458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002828771,"about_ca_topic_score_gemma":0.0002682632,"domain_scores_codex":[0.9941825,0.0000462461,0.0006406702,0.002979161,0.0011669,0.0009844918],"domain_scores_gemma":[0.9926824,0.0008585245,0.0004014424,0.005495297,0.0003198577,0.0002424822],"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":[0.00001726618,0.00004436599,0.00001468384,0.0002362679,0.00002834727,0.00006802705,0.0001601249,0.0216292,0.00003196141,0.01017184,0.002442396,0.9651555],"study_design_scores_gemma":[0.0004870965,0.00009352063,0.000009129816,0.000335582,0.00002347966,0.00001331651,6.571935e-8,0.8880533,0.0004984471,0.03251571,0.07718881,0.0007815405],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001492432,0.0001030362,0.9890901,0.004161916,0.003884765,0.0008644878,0.0001662026,0.000303377,0.001411166],"genre_scores_gemma":[0.004033378,0.00001274494,0.9809785,0.0111609,0.00178325,0.00002448095,0.0004023163,0.00007639297,0.00152799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9643739,"threshold_uncertainty_score":0.9996504,"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."}}