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Record W2112682457 · doi:10.4319/lo.2008.53.2.0728

Spatial variability of climate and land‐use effects on lakes of the northern Great Plains

2008· article· en· W2112682457 on OpenAlexafffundabout
Samantha V. Pham, Peter R. Leavitt, Suzanne McGowan, Pedro R. Peres‐Neto

Bibliographic record

VenueLimnology and Oceanography · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversité de MontréalUniversity of Regina
FundersAgriculture and Agri-Food Canada
KeywordsEnvironmental scienceEcosystemLake ecosystemPrecipitationSpatial variabilitySalinityHydrology (agriculture)EcologyClimate changeLand usePhysical geographyGeologyGeography

Abstract

fetched live from OpenAlex

Evaluation of the effects of climate change and human activities on lakes requires improved understanding of how stressors interact and the degree to which individual sentinel lakes represent broad spatial patterns of ecosystem response to disturbance. We surveyed modern water chemistry (major ions, conductivity, salinity, lake volume) and sediments (algal pigments, stable isotopes) in 21 lakes that surround Humboldt Lake, Saskatchewan, site of a 2,000‐yr climate reconstruction, to quantify spatial synchrony (S, the mean among‐lake correlation coefficient) of prairie lake response to climate variability, land use, and their interactions. Whole‐lake mass balances of total dissolved substances constructed at each site revealed that evaporation of water controlled seasonal changes in salt content only in years with dry summers (2003), leading to widespread spatial coherence of ecosystems (S = 0.78). In contrast, variations in hydrologic inputs (precipitation, groundwater) and solute fluxes regulated salt balances of lakes during years with wet summers (2004, 2005) and substantially reduced lake synchrony (S = 0.13‐0.58). Furthermore, >25% of sites exhibited increased nitrogen influx (as d15N) and cyanobacterial production (as fossil pigments) between ca. 1920 and 2003, with particularly strong effects of land use recorded for northeastern sites, where evaporative forcing was greatest. Finally, principal component and canonical ordinations with redundancy analysis both explained ~50% of the variance in lake sensitivity to climate and land use and revealed that the effects of climate and land use interacted strongly, but that the unique effects of each factor remained identifiable in modern lake surveys.

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 score0.002
Threshold uncertainty score0.220

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 head0.005
GPT teacher head0.177
Teacher spread0.172 · 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

Citations123
Published2008
Admission routes3
Has abstractyes

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