{"id":"W4391270950","doi":"10.1093/nsr/nwae036","title":"Thermoelectrocatalysis: an emerging strategy for converting waste heat into chemical energy","year":2024,"lang":"en","type":"article","venue":"National Science Review","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Center for Integrated Quantum Science and Technology; Southern University of Science and Technology; Jiangsu University; State Key Laboratory of New Ceramics and Fine Processing; Beihang University; Tsinghua University; National Natural Science Foundation of China; Institut national de la recherche scientifique","keywords":"Waste heat; Perspective (graphical); Heat energy; Energy (signal processing); Waste-to-energy; Environmental science; Waste management; Clean energy; Process engineering; Biochemical engineering; Computer science; Engineering; Environmental engineering; Mechanical engineering; Municipal solid waste; 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":[0.0006844247,0.0006198363,0.0005826839,0.0008915667,0.0003188377,0.001566887,0.0007009766,0.001379062,0.001387396],"category_scores_gemma":[0.0003039487,0.0002292502,0.0003820424,0.0008475581,0.001259715,0.002479074,0.0007531819,0.001573718,0.0007037447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000848791,"about_ca_system_score_gemma":0.00103909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004684564,"about_ca_topic_score_gemma":0.001513815,"domain_scores_codex":[0.9996724,0.00004056427,0.00001271389,0.00005148591,0.0001725754,0.00005022182],"domain_scores_gemma":[0.9998968,0.00004296886,0.00001385066,0.000006083746,0.00002985662,0.00001055468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008781495,0.0001977009,0.0005025283,0.004851016,0.000116759,0.0006729877,0.0003162733,0.001342074,0.1874009,0.3904094,0.02141196,0.3926905],"study_design_scores_gemma":[0.00001769743,0.0002989811,0.0003994489,0.0006602928,0.00005805987,0.001374127,0.0003364772,0.001725944,0.09738077,0.05044785,0.8472626,0.0000378331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.01278756,0.8933694,0.03923511,0.01130029,0.003013416,0.00005337167,0.00009197766,0.0001343217,0.04001463],"genre_scores_gemma":[0.07897811,0.8944731,0.01110099,0.002946583,0.001202955,0.00007191995,0.0000970757,0.00003490115,0.01109438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001566887,"threshold_uncertainty_score":0.006158412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03089706599711525,"score_gpt":0.3640701189500532,"score_spread":0.333173052952938,"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."}}