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Impacts of Climate Warming and Nitrogen Deposition on Alpine Plankton in Lake and Pond Habitats: an In Vitro Experiment

2008· article· en· W2117307224 on OpenAlexaffabout
Patrick L. Thompson, Marie-Claire St-Jacques, Rolf D. Vinebrooke

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPlanktonEcologyGlobal warmingEnvironmental scienceTrophic levelClimate changeEcosystemHerbivorePhytoplanktonHabitatBiologyNutrient

Abstract

fetched live from OpenAlex

The combined effects of multiple ecological stressors determine the net impact of global change on environmentally sensitive alpine and polar environments.For example, climate warming and nitrogen deposition both increasingly affect ecosystems at high elevations.We hypothesized that the net impact of warming and nitrogen on alpine plankton differs between consumers and producers because of the greater environmental sensitivity of higher trophic levels.Also, we expected that habitat conditions would mediate the responses of plankton to these two stressors as sediments function as ecological buffers against environmental change.These hypotheses were tested in a growth chamber by applying temperature (8 vs. 15uC) and nitrogen (200 vs. 1000 mg N L 21 ) treatments to a planktonic alpine community in the presence and absence of sediments obtained from Pipit Lake, Banff National Park, Alberta.A significant nitrogen-temperature interaction affected phytoplankton abundance because the positive effect of fertilization depended on warming.Warming also amplified the effect of nitrogen on herbivores while suppressing the fecundity of omnivores.The presence of sediments suppressed the positive effect of warming on herbivores, but stimulated omnivores.The observed prevalence of non-additive effects highlights the strong potential for global change causing future ecological surprises in alpine environments.

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)0.001
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 score0.007
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.304
Teacher spread0.276 · 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

Citations27
Published2008
Admission routes2
Has abstractyes

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