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Record W4409987418 · doi:10.1016/j.ecolind.2025.113497

Impact of climatic factors on eutrophication in the World’s largest lake

2025· article· en· W4409987418 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEcological Indicators · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsEutrophicationEcologyEnvironmental scienceGeographyBiologyNutrient

Abstract

fetched live from OpenAlex

• We explore impact of climatic factors on eutrophication in the world’s largest lake. • Data from MODIS-Aqua and the ERA5 model, spanning 2003 to 2021, were used. • We used the GAM to understand dynamics of Chl- a in response to the changing climate. • Photosynthetically active radiation dominantly impacted Chl- a changes in the lake. Climatic and anthropogenic factors both contribute to lake eutrophication. However, the influence of climatic factors, particularly in large, deep, and transboundary lakes, remains poorly understood due to technical challenges, data scarcity, and geopolitical constraints. This is especially true for the Caspian Sea, the world’s largest lake, where its unique continental climate further complicates efforts to quantify the climate contribution to eutrophication. This study leverages extensive datasets from MODIS-Aqua and the ERA5, spanning 2003 to 2021, to develop a generalized additive model (GAM) aimed at investigating the impact of climatic factors on chlorophyll- a (Chl- a ) concentrations in the Caspian Sea. Given the sea’s distinct continental climate, complex morphometric characteristics, and significant spatial variability in Chl- a , the basin was divided into 14 subzones to better capture regional responses of Chl- a to climatic changes. The GAM, trained to predict Chl- a , demonstrated acceptable performance (correlation coefficient > 0.5) in 12 of the 14 subzones. Results indicate the predominant influence of photosynthetically active radiation on Chl- a changes in nine subzones, particularly in the southern Caspian Sea. This parameter is critical for regulating light availability for phytoplankton productivity. Sea surface temperature emerged as the second most influential driver of Chl- a levels, likely due to its role in controlling thermal stratification and upwelling, which stimulate phytoplankton growth. Precipitation, by contrast, was found to be the least significant driver of Chl- a levels during the study period. By elucidating the relationships between climatic drivers and Chl- a levels, this study provides a comprehensive understanding of the complex dynamics of eutrophication under changing climate conditions in the Caspian Sea.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.286
Teacher spread0.274 · 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