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Record W4414568638 · doi:10.1016/j.jglr.2025.102685

Centuries of change: Salinity and carbon dynamics in a large shallow lake

2025· article· en· W4414568638 on OpenAlexvenueno aff
Lajos Vörös, György Tóth, Zsófia Látrányi-Lovász, Boglárka Somogyi

Bibliographic record

VenueJournal of Great Lakes Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
FundersNemzeti Kutatási Fejlesztési és Innovációs HivatalMagyar Tudományos Akadémia
KeywordsCalciteSalinityBiogeochemical cyclePrecipitationFlux (metallurgy)SulfateTotal inorganic carbonCarbon fibersCarbon cycle

Abstract

fetched live from OpenAlex

This study examines over a century of salinity changes in Lake Balaton, the largest shallow lake in Central Europe. Total ion concentrations have increased from a historical baseline of 450 mg/L to approximately 650 mg/L today, with an annual rise of 5.0 mg/L in the largest basin since the 1970s. Notable annual increases include magnesium (0.7 mg/L), sodium (0.6 mg/L), chloride (0.7 mg/L), sulfate (1.8 mg/L), and bicarbonate-carbonate ions (1.0 mg/L). In contrast, calcium levels have remained stable due to substantial calcite precipitation, which reduces the calcium content of inflowing waters by over half. Calcite precipitation in Lake Balaton varies significantly, ranging from 25,000 to 125,000 tons/year, with an average of 75,000 tons/year between 2010 and 2022. Biologically induced calcite precipitation, driven by photosynthesis, occurs seasonally during summer but dissolves again in autumn and winter. However, the majority of calcite precipitation is non-biogenic, driven by the equilibration of CO 2 -supersaturated inflows with atmospheric CO 2 . This process releases approximately 33,000 tons of CO 2 annually, underscoring Lake Balaton’s contribution to global carbon cycling. These findings highlight the combined impact of natural processes and anthropogenic influences on Lake Balaton’s salinity and biogeochemical dynamics, emphasizing its importance as a model for understanding the broader implications of freshwater salinization and carbon flux in shallow lakes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.328
Teacher spread0.286 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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