{"id":"W2799298886","doi":"10.1109/tvt.2018.2833447","title":"Battery Recharge Time of a Stochastic Linear and Nonlinear Energy Harvesting Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Groundwater recharge; Stochastic process; Nonlinear system; Monte Carlo method; Energy (signal processing); Stochastic modelling; Applied mathematics; Battery (electricity); Energy harvesting; Computer science; Mathematical optimization; Mathematics; Engineering; Power (physics); Physics; 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":[0.001036269,0.000333875,0.0005714129,0.0004022171,0.0004036558,0.0007198636,0.0006088408,0.0006326658,0.002505592],"category_scores_gemma":[0.003748121,0.000240051,0.0005181066,0.0004162774,0.001060804,0.001036612,0.0005913529,0.0006037937,0.000141298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009267614,"about_ca_system_score_gemma":0.0005870176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002961964,"about_ca_topic_score_gemma":0.001732811,"domain_scores_codex":[0.9997019,0.00006132937,0.00001901893,0.00005350201,0.00008046653,0.00008376688],"domain_scores_gemma":[0.9981638,0.001240404,0.0002799008,0.00006515362,0.0001747207,0.00007611565],"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":[0.000252537,0.00004426179,0.00288987,0.0001250092,0.00004939158,0.0006585481,0.0002057033,0.9325889,0.007916197,0.04895132,0.0004535572,0.005864808],"study_design_scores_gemma":[0.000006614465,0.00002715385,0.000644203,0.000005408159,0.00001160371,0.00007121421,0.00004684442,0.9943767,0.0008204021,0.003860291,0.0001205602,0.000009192546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.752676,0.001051479,0.2336915,0.00089641,0.00006195808,0.00006739643,0.0002113976,0.0001992766,0.0111446],"genre_scores_gemma":[0.9971937,0.0001120042,0.0009369978,0.00002003316,0.000007649594,0.00001074845,0.00002517177,0.000009307892,0.001684498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002961964,"threshold_uncertainty_score":0.008382082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007130397652046363,"score_gpt":0.1958639950702325,"score_spread":0.1887335974181861,"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."}}