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Record W7127984969 · doi:10.26511/jkset.26.6.14

Climate Change Vulnerability Assessments Based on Korean Freshwater Fish and Comparative Evaluations with three International Models

2025· article· W7127984969 on OpenAlexaboutno aff
Hyeji Choi, Min Jae Cho, Sung-ryong Kang, Kwang-Guk An

Bibliographic record

VenueJournal of the Korean Society for Environmental Technology · 2025
Typearticle
Language
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeVulnerability assessmentVulnerability (computing)Freshwater fishEctothermWeightingMetric (unit)Spearman's rank correlation coefficient

Abstract

fetched live from OpenAlex

Potential candidate fish species vulnerable to climate change were identified by the pre-screening process in streams and rivers of 4 major watersheds in Korea. Total 51 candidates from 252 fish species were selected by eliminating the species such as estuary-dwelling species, tolerant species to water pollution, omnivorous species (generalist), and widely distributed species. And then we preliminarily considered the restricted headwater species, cold-water affinity species, limited dispersal ability, and sensitive species to thermal and hydrological variations. The selected species were evaluated using three internationally recognized climate change vulnerability assessment tools (US Midwestern, US Environmental Protection Agency, and Canada models) to compare species-level vulnerability patterns. Spearman rank correlation analysis was used to quantitatively compare the consistency of assessment results across models. While three models identified species as relatively consistently vulnerable to climate change, differences in metric composition and weighting methods resulted in variations in relative vulnerability rankings for some species, highlighting the limitations of a single assessment model. A total of 92 metrics reported in global literature were reviewed, from which 20 metrics were selected by considering domestic data availability, ecological relevance, and sensitivity to climate drivers. The selected metrics were organized into three categories: temperature exposure, sensitivity, and adaptability. A standardized three-level scoring system (1, 3, and 5) was applied to assess the climate change vulnerability on the selected 51 species. Considering that freshwater fish were ectothermic organisms and exhibited high physiological sensitivity to water temperature changes, weighting of each metric was applied to metrics directly related to water temperature, thereby more clearly distinguishing the vulnerabilities of cold-water species. Overall, assessment and prioritization of climate change vulnerability are fundamental to identifying at-risk freshwater fish species and guiding effective conservation and management strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.306
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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