{"id":"W2009245774","doi":"10.1109/vetecf.2010.5594518","title":"Optimal Management of Rechargeable Biosensors in Temperature-Sensitive Environments","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Biosensor; Markov decision process; Heuristic; Wireless; Computer science; Process (computing); Mathematical optimization; Constraint (computer-aided design); Exponential function; Markov process; Biological system; Materials science; Nanotechnology; Mathematics; Engineering; Statistics; Telecommunications; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001784868,0.0008673783,0.001276573,0.0005269664,0.0005397006,0.001126885,0.001215987,0.00108026,0.001362059],"category_scores_gemma":[0.004044936,0.0005829592,0.000464868,0.000450512,0.00123445,0.001624481,0.0008644345,0.0006924298,0.0001696809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0014893,"about_ca_system_score_gemma":0.001281952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004050843,"about_ca_topic_score_gemma":0.003189971,"domain_scores_codex":[0.9990288,0.0003275832,0.00004463809,0.0001963213,0.0001448567,0.0002578644],"domain_scores_gemma":[0.9966163,0.002398836,0.0004010914,0.0001107908,0.0002611823,0.0002118813],"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":[0.000151672,0.00006016577,0.0004745373,0.00003416787,0.00001849235,0.00008410952,0.00004175833,0.9835353,0.002357074,0.004373087,0.0003569199,0.008512583],"study_design_scores_gemma":[0.00001948897,0.00005310145,0.0001702958,0.000003943872,0.000009580351,0.00001740943,0.00002111883,0.9934274,0.0008485665,0.005269078,0.0001529499,0.000007064647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3211032,0.001787979,0.6689028,0.001595234,0.00009565483,0.0001787824,0.0001333672,0.0005024733,0.005700375],"genre_scores_gemma":[0.9848812,0.0002385339,0.01366665,0.00007728628,0.00001236415,0.0000433648,0.00002849577,0.0000194541,0.001032698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004050843,"threshold_uncertainty_score":0.01080567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004641645935466137,"score_gpt":0.1852522852136005,"score_spread":0.1806106392781343,"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."}}