{"id":"W3000275637","doi":"10.1109/tnse.2020.2966504","title":"Data Collection Versus Data Estimation: A Fundamental Trade-Off in Dynamic Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Mathematical optimization; Markov decision process; Data collection; Convergence (economics); Data quality; Markov process; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0205774,0.001248625,0.002237459,0.001302336,0.00146136,0.005194183,0.003760403,0.00386079,0.001713402],"category_scores_gemma":[0.0819521,0.00137297,0.000743643,0.00245198,0.004461761,0.01513358,0.005042782,0.004296264,0.0003260784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002590953,"about_ca_system_score_gemma":0.002765505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201773,"about_ca_topic_score_gemma":0.001778694,"domain_scores_codex":[0.9866579,0.007116186,0.0008646404,0.002557387,0.002196121,0.0006077066],"domain_scores_gemma":[0.8918756,0.09570216,0.003814491,0.005321235,0.002053955,0.001232492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004926923,0.0001480402,0.003107459,0.0005425573,0.0001719373,0.0004389047,0.0008688624,0.4365985,0.00312447,0.4037942,0.001716803,0.1489955],"study_design_scores_gemma":[0.00004368226,0.0001584908,0.0006668449,0.0001129334,0.00004082483,0.0002431308,0.0002151856,0.6813519,0.001541309,0.3131626,0.002400323,0.00006271324],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01554198,0.00177499,0.9761825,0.003683571,0.00007147858,0.0000877702,0.00006929421,0.0001210034,0.002467405],"genre_scores_gemma":[0.7608994,0.002982323,0.2322916,0.0008488715,0.0003727958,0.0004079834,0.0001808626,0.0001475385,0.001868673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0205774,"threshold_uncertainty_score":0.108825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471512441312536,"score_gpt":0.255216889700903,"score_spread":0.2205017652877777,"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."}}