Machine learning based disruption prediction using long short-term memory in KSTAR
- DOI
- 10.1088/1741-4326/adeb9b
- Published
- 2025-07-15
- Container
- Nuclear Fusion
- Publisher
- IOP Publishing
- 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:10.1088/1741-4326/adeb9b,
title = {Machine learning based disruption prediction using long short-term memory in KSTAR},
author = {Jeongwon Lee and Jayhyun Kim and Jinsu Kim and Sang-hee Hahn and Hyunsun Han and Giwook Shin and Yong-Su Na and Yong Un Nam},
year = {2025},
journal = {Nuclear Fusion},
doi = {10.1088/1741-4326/adeb9b},
url = {https://doi.org/10.1088/1741-4326/adeb9b}
}RIS
TY - JOUR TI - Machine learning based disruption prediction using long short-term memory in KSTAR AU - Jeongwon Lee AU - Jayhyun Kim AU - Jinsu Kim AU - Sang-hee Hahn AU - Hyunsun Han AU - Giwook Shin AU - Yong-Su Na AU - Yong Un Nam PY - 2025 JO - Nuclear Fusion DO - 10.1088/1741-4326/adeb9b UR - https://doi.org/10.1088/1741-4326/adeb9b ER -
APA
Lee, J., Kim, J., Kim, J., Hahn, S., Han, H., Shin, G., Na, Y., & Nam, Y. U. (2025). Machine learning based disruption prediction using long short-term memory in KSTAR. Nuclear Fusion. https://doi.org/10.1088/1741-4326/adeb9b
Source records
- crossref · retrieved 2026-09-26T15:40:30.999Z