{"id":"W3157791361","doi":"10.1109/jiot.2021.3074823","title":"Centipede: Leveraging the Distributed Camera Crowd for Cooperative Video Data Storage","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Centipede; Distributed data store; Distributed database; Computer graphics (images); Multimedia; 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":[0.001114055,0.000198318,0.0003039692,0.0001011353,0.0002189728,0.0008319768,0.005099275,0.00007942745,0.00002115859],"category_scores_gemma":[0.0009019991,0.0001468455,0.0001129579,0.0004544626,0.0001045082,0.001236213,0.001652505,0.0006537905,0.000008984058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001252433,"about_ca_system_score_gemma":0.0002083184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000434948,"about_ca_topic_score_gemma":0.00001672278,"domain_scores_codex":[0.9980276,0.0001182054,0.000504508,0.0004670017,0.000453968,0.000428736],"domain_scores_gemma":[0.997348,0.0003468103,0.0003477034,0.001210371,0.0006418678,0.0001052747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009162227,0.000214916,0.0004819546,0.00005394897,0.000629661,0.0005069741,0.01092309,0.0070237,0.01973487,0.005815317,0.8892854,0.06523854],"study_design_scores_gemma":[0.001583187,0.0003975434,0.0005989788,0.0007625562,0.00008752814,0.0024118,0.001496899,0.5498274,0.1909124,0.007307357,0.2438019,0.0008124372],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05237507,0.0004579742,0.931615,0.01319885,0.001945108,0.0001573215,0.00005860667,0.0001135889,0.00007847832],"genre_scores_gemma":[0.9287032,0.00003555963,0.06872544,0.001407269,0.0002199931,0.000008657936,0.00002009302,0.00001788042,0.0008618636],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8763282,"threshold_uncertainty_score":0.9475806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04931258291752986,"score_gpt":0.2947340593494496,"score_spread":0.2454214764319198,"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."}}