{"id":"W3033798427","doi":"10.1109/tcsii.2020.2999331","title":"83.9% Efficiency 100-mV Self-Startup Boost Converter for Thermoelectric Energy Harvester in IoT Applications","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maximum power point tracking; Boost converter; Ripple; Thermoelectric generator; Power (physics); Computer science; Voltage; Generator (circuit theory); Internal resistance; Maximum power principle; Photovoltaic system; Electrical efficiency; Electrical engineering; Electronic engineering; Engineering; Thermoelectric effect; Inverter; Battery (electricity); Physics","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":[0.0001689989,0.0002747445,0.0003255342,0.0003113422,0.0002155234,0.0003983017,0.0004579428,0.0002623056,0.003713796],"category_scores_gemma":[0.0001324262,0.0001587835,0.000202444,0.0004627445,0.0001115179,0.0005074907,0.0002446575,0.0004301161,0.001737028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041293,"about_ca_system_score_gemma":0.0001692165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001137225,"about_ca_topic_score_gemma":0.0002949481,"domain_scores_codex":[0.9999013,0.000007399812,0.000005255631,0.00001700966,0.0000562593,0.00001273673],"domain_scores_gemma":[0.9999602,0.00000789223,0.000005969994,0.000005612314,0.00001593913,0.000004377383],"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":[0.0001285863,0.00006033434,0.000556415,0.0005062328,0.00002080177,0.0002927697,0.00009983299,0.0008960893,0.874067,0.005598674,0.004133117,0.1136401],"study_design_scores_gemma":[0.00002458281,0.0003027415,0.002761047,0.00007142052,0.00004790166,0.001717192,0.00006532231,0.01774857,0.8958347,0.001725785,0.07967243,0.00002824961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.354471,0.01313682,0.4989646,0.001061738,0.0008849707,0.0003592031,0.0009437278,0.004409003,0.125769],"genre_scores_gemma":[0.9112043,0.003098602,0.0571081,0.0002115599,0.0001098306,0.0001146044,0.0003885556,0.0002494126,0.02751505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003713796,"threshold_uncertainty_score":0.01242387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996942888868497,"score_gpt":0.212690905571035,"score_spread":0.19272147668235,"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."}}