{"id":"W2980810818","doi":"10.3390/en12203942","title":"Human Body Heat Based Thermoelectric Harvester with Ultra-Low Input Power Management System for Wireless Sensors Powering","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Loughborough University","keywords":"Energy harvesting; Thermoelectric generator; Electrical engineering; Wireless sensor network; Power management; Electricity; Microcontroller; Sensor node; Energy storage; Electric potential energy; Voltage; Power (physics); Thermoelectric effect; Wireless; Automotive engineering; Engineering; Computer science; Telecommunications; Wireless network; Key distribution in wireless sensor networks; Physics","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":[0.0001051739,0.0001857011,0.0002460406,0.0002052338,0.000132507,0.0002468,0.000626267,0.0003021752,0.002799649],"category_scores_gemma":[0.00015336,0.0001121574,0.0002045746,0.0002142925,0.0001329994,0.0004279791,0.000222802,0.0002191261,0.0006484495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307669,"about_ca_system_score_gemma":0.0001020335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001091649,"about_ca_topic_score_gemma":0.0002188829,"domain_scores_codex":[0.9999115,0.00001407881,0.0000065276,0.00002527646,0.00003301319,0.000009536303],"domain_scores_gemma":[0.9999338,0.00001618862,0.0000125834,0.00001058008,0.00002200727,0.000004698684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002873402,0.0001066727,0.001510851,0.0005307582,0.00005782752,0.0004676917,0.0001782851,0.002087417,0.7940049,0.002747173,0.005512989,0.1925081],"study_design_scores_gemma":[0.00009138816,0.00161123,0.00894755,0.0001188083,0.0001651033,0.003008381,0.0001586492,0.09271321,0.8220674,0.002584091,0.06844978,0.00008439522],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3320155,0.004827983,0.6397181,0.0007924116,0.0006196265,0.0002296682,0.0003970491,0.003614107,0.01778571],"genre_scores_gemma":[0.9419587,0.001080315,0.03823265,0.0004155845,0.00009596515,0.000112017,0.0002010447,0.00007426221,0.01782935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002799649,"threshold_uncertainty_score":0.009365797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00463385308059062,"score_gpt":0.2135885222354264,"score_spread":0.2089546691548358,"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."}}