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Record W4285408115 · doi:10.1002/cnl2.16

Carbon Neutralization: The exploration of clean energy and ecological environment to achieve low carbon emission

2022· article· en· W4285408115 on OpenAlexaffabout
Rose Amal, Shulei Chou, Min Zhao, Dawei Wang, Jianbing Li, Hui Ying Yang

Bibliographic record

VenueCarbon Neutralization · 2022
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsClean energyCarbon fibersEnvironmental scienceNeutralizationEcologyEnvironmental protectionMaterials scienceBiology

Abstract

fetched live from OpenAlex

Carbon Neutralization: The exploration of clean energy and ecological environment to achieve low carbon emissionClimate change is already affecting the entire world, with extreme weather conditions due to burning fossil fuels like coal, oil and gas.In this context, the term "Carbon Neutralization" is proposed.The term is used in the context of carbon dioxide-releasing processes associated with transportation, energy production, agriculture, and industry.Carbon Neutralization means having a balance between emitting carbon and absorbing carbon from the atmosphere in carbon sinks.Switching energy systems from fossil fuels to renewables like solar or wind will reduce the emissions driving climate change.Carbon Neutralization is copublished by Wiley and Wenzhou University, China.Carbon Neutralization is an international journal that addresses the growing scientific interests and needs in cutting-edge energy technology involving carbon utilization and carbon emission control.It serves as a high-quality platform for researchers working in a wide variety of scientific areas to communicate their findings and critical opinions as well as bring the communities of advanced material and energy together to contribute to this emerging field.Carbon Neutralization aims at publishing environmental science, ecosystems, carbon capture and storage, renewable energy, solar energy, fuel cells, batteries, hydrogen energy, energy harvesting devices, bioenergy, biofuels, electrocatalysis, photocatalysis, and so forth.It prompts new technologies leading to the control of carbon emission and green production of carbon materials.The journal recognizes the complexity of issues, and therefore particularly welcomes innovative interdisciplinary research with wide impact.Carbon Neutralization invites you to submit original research and review articles, editorials, short communications, and letters to the editor.We encourage contributors to read the authors' guidelines (https://onlinelibrary

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0140.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.243
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2022
Admission routes2
Has abstractyes

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