{"id":"W2019796561","doi":"10.1109/infcom.2013.6566962","title":"Data loss and reconstruction in sensor networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":211,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Missing data; Wireless sensor network; Interpolation (computer graphics); Data mining; Compressed sensing; Stability (learning theory); Noise (video); Signal reconstruction; Data loss; Artificial intelligence; Machine learning; Signal processing","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":[0.00002898336,0.00004064368,0.00004725119,0.00004245064,0.0000105262,0.00002065686,0.00006591837,0.00005878239,0.0001135826],"category_scores_gemma":[0.00001157506,0.00003606285,0.000002806777,0.00008016351,0.00002416687,0.000174552,0.00003629821,0.0000594679,0.00001987444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007693909,"about_ca_system_score_gemma":0.000001010463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003783219,"about_ca_topic_score_gemma":0.00005671012,"domain_scores_codex":[0.999746,0.000003073453,0.00007623329,0.00007318263,0.00001915789,0.00008230237],"domain_scores_gemma":[0.9998019,0.00001088527,0.000004362176,0.0001648029,0.000007170805,0.00001083395],"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":[0.000002251785,0.000009127418,0.100286,0.00005499905,0.00002246717,0.00000632937,0.00007586527,0.0987066,0.0004688422,0.003040066,0.0168031,0.7805244],"study_design_scores_gemma":[0.0001167205,0.000002986089,0.009105525,0.000007875798,0.00000123937,0.00001926427,0.0002036586,0.9885387,0.0005874272,0.0003746942,0.0009604364,0.00008148424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8221108,0.000360765,0.1655702,0.0002403712,0.0004130121,0.0002271068,0.000006438873,0.001218043,0.009853219],"genre_scores_gemma":[0.9955019,0.0002214602,0.004144363,0.00002086261,0.00001940119,0.000003891224,0.000009706674,0.000006158694,0.00007224835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8898321,"threshold_uncertainty_score":0.14706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263546755693384,"score_gpt":0.1996841389948898,"score_spread":0.1870486714379559,"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."}}