Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models.

Zhang X, Zhang D, Leye A, Scott A, Visser L, Ge Z, Bonnington P

DOI: 10.3390/s22010160
Published: 2021 Dec 27
Container: Sensors (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.3390/s22010160,
  title = {Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models.},
  author = {Zhang X and Zhang D and Leye A and Scott A and Visser L and Ge Z and Bonnington P},
  year = {2021},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s22010160},
  url = {https://doi.org/10.3390/s22010160}
}

RIS

TY  - JOUR
TI  - Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models.
AU  - Zhang X
AU  - Zhang D
AU  - Leye A
AU  - Scott A
AU  - Visser L
AU  - Ge Z
AU  - Bonnington P
PY  - 2021
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s22010160
UR  - https://doi.org/10.3390/s22010160
ER  -

APA

X, Z., D, Z., A, L., A, S., L, V., Z, G., & P, B. (2021). Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s22010160

Source records

pubmed · retrieved 2026-09-26T10:49:08.178Z