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Record W2008479140 · doi:10.4319/lo.2008.53.1.0404

Cooling lakes while the world warms: Effects of forest regrowth and increased dissolved organic matter on the thermal regime of a temperate, urban lake

2008· article· en· W2008479140 on OpenAlexaffabout
Tanentzap Andrew J., Norman D. Yan, Bill Keller, R. Girard, Jocelyne Heneberry, John M. Gunn, David P. Hamilton, Peter A. Taylor

Bibliographic record

VenueLimnology and Oceanography · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsMinistry of the Environment, Conservation and ParksLaurentian UniversityYork University
FundersMinistry of Environment
KeywordsEnvironmental scienceTemperate climateDissolved organic carbonAttenuationWind speedAtmospheric sciencesClimate changeHydrology (agriculture)OceanographyEcologyGeology

Abstract

fetched live from OpenAlex

Depending on the magnitudes and directions of changes in air temperatures, winds, and underwater light attenuation, lakes may either warm or cool. Here we report a 28‐yr decrease in the whole‐lake average temperature of Clearwater Lake, Canada, despite regional signatures of climate warming. Using a one‐dimensional lake mixing model, we demonstrate that this pattern was attributable to a 35% reduction in surface wind speeds, itself explained by forest regrowth following local SO 2 emission reductions and tree planting, and a 10‐fold increase in dissolved organic carbon concentrations causing a substantial increase in vertical light attenuation following deacidification of the lake. Long‐term trends in lake temperatures do not necessarily follow those of air temperatures. The Clearwater Lake data demonstrate that any factors that influence local wind speeds and underwater light attenuation should be considered as modifiers of the effects of climate warming on lake thermal regimes.

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 score0.001
Threshold uncertainty score0.282

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 studies0.0000.001
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 head0.005
GPT teacher head0.171
Teacher spread0.166 · 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

Citations117
Published2008
Admission routes2
Has abstractyes

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