{"id":"W2091972047","doi":"10.1016/j.sigpro.2010.05.019","title":"Joint source-decoding in large scale sensor networks using Markov random field models","year":2010,"lang":"en","type":"article","venue":"Signal Processing","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Decoding methods; Computer science; Markov process; Markov random field; Algorithm; Scalability; Joint (building); Markov chain; Quantization (signal processing); Vector quantization; Artificial intelligence; Mathematics; Machine learning; Statistics; Engineering","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":[0.0007438752,0.0002152496,0.0002958238,0.0001708636,0.0003877677,0.0005648286,0.0003680296,0.0002043548,0.00004513425],"category_scores_gemma":[0.00002763901,0.0002074367,0.000102979,0.0006817183,0.0000351179,0.0008244338,0.0001867259,0.0007739331,0.00000477475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003839639,"about_ca_system_score_gemma":0.00007072686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003583989,"about_ca_topic_score_gemma":0.00004820281,"domain_scores_codex":[0.9980879,0.00005683621,0.0004300275,0.0005016041,0.0002898477,0.000633806],"domain_scores_gemma":[0.9991833,0.0001114725,0.0001680166,0.0002700039,0.0001127278,0.0001544766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007579433,0.0001191697,0.0003728177,0.00004053928,0.00001188466,0.00005200929,0.0007229152,0.5984806,0.007736482,0.0001919789,0.0001532264,0.3920425],"study_design_scores_gemma":[0.0009678795,0.00002205804,0.00002749312,0.0000977071,0.000006157393,0.0000596913,0.000104124,0.9954961,0.001764265,0.0009801204,0.0002038408,0.0002705736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04520749,0.0001603593,0.9529192,0.0001404571,0.0004236292,0.0001474614,0.000001387675,0.0002024279,0.0007976035],"genre_scores_gemma":[0.9228311,0.000005432841,0.0763205,0.0003782054,0.0003595059,0.000005949799,0.00000219322,0.00002061987,0.00007648754],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8776236,"threshold_uncertainty_score":0.8459022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163436623336527,"score_gpt":0.2379779742394222,"score_spread":0.2216343119057695,"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."}}