Global Quantification of Black Carbon in Seasonal Snow: A Physically and Observationally Constrained Machine-Learning Framework
- DOI
- 10.1021/acs.est.5c09936
- Published
- 2026-02-05
- Container
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1021/acs.est.5c09936,
title = {Global Quantification of Black Carbon in Seasonal Snow: A Physically and Observationally Constrained Machine-Learning Framework},
author = {Yang Chen and Shirui Yan and Yaliang Hou and Yongxiang Lin and Kexin Liu and Dingfan Cao and Yuxuan Xing and Daizhou Zhang and Wei Pu and Xin Wang},
year = {2026},
journal = {Environmental Science \& Technology},
doi = {10.1021/acs.est.5c09936},
url = {https://doi.org/10.1021/acs.est.5c09936}
}RIS
TY - JOUR TI - Global Quantification of Black Carbon in Seasonal Snow: A Physically and Observationally Constrained Machine-Learning Framework AU - Yang Chen AU - Shirui Yan AU - Yaliang Hou AU - Yongxiang Lin AU - Kexin Liu AU - Dingfan Cao AU - Yuxuan Xing AU - Daizhou Zhang AU - Wei Pu AU - Xin Wang PY - 2026 JO - Environmental Science & Technology DO - 10.1021/acs.est.5c09936 UR - https://doi.org/10.1021/acs.est.5c09936 ER -
APA
Chen, Y., Yan, S., Hou, Y., Lin, Y., Liu, K., Cao, D., Xing, Y., Zhang, D., Pu, W., & Wang, X. (2026). Global Quantification of Black Carbon in Seasonal Snow: A Physically and Observationally Constrained Machine-Learning Framework. Environmental Science & Technology. https://doi.org/10.1021/acs.est.5c09936
Source records
- crossref · retrieved 2026-09-27T09:51:30.217Z