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Record W4410260722 · doi:10.1002/lol2.70021

Climate change and underwater light: Large‐scale changes in ultraviolet radiation transparency associated with intensifying wet–dry cycles

2025· article· en· W4410260722 on OpenAlexafffund
Shohei Watanabe, Erin P. Overholt, Geoffrey Schladow, Warwick F. Vincent, Craig E. Williamson

Bibliographic record

VenueLimnology and Oceanography Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersDivision of Environmental BiologyDivision of Biological InfrastructureNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsTransparency (behavior)Environmental scienceClimate changeRadiationUnderwaterScale (ratio)Atmospheric sciencesUltraviolet radiationClimatologyMeteorologyGeographyOceanographyOpticsPhysicsGeologyChemistryComputer scienceCartographyRadiochemistry

Abstract

fetched live from OpenAlex

Abstract Ultraviolet radiation (UV) is the most energetic waveband of incident solar radiation and has wide‐ranging effects in the aquatic environment. Our analysis of an 18‐year record of underwater irradiance and related limnological variables in sub‐alpine, ultra‐oligotrophic Lake Tahoe revealed orders of magnitude changes in UV transparency associated with interannual climate perturbations. The large‐scale shifts between years were caused by pronounced changes in the loading of allochthonous particulate matter and colored dissolved organic matter associated with regional dry–wet cycles, while autochthonous factors explained the seasonal variations in UV under average weather conditions. Water clarity in the photosynthetically available radiation (PAR) waveband showed less variation, resulting in large interannual differences in the UV : PAR ratio. Clearwater lakes are likely to experience increasingly large fluctuations in underwater UV and spectral irradiance due to ongoing climate change and precipitation extremes, with potential impacts on their ecosystem structure and function.

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)0.000
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 score0.045
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.189
Teacher spread0.179 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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