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Ultraviolet and temperature effects on planktonic rotifers and crustaceans in northern temperate lakes

2005· article· en· W2056933911 on OpenAlexfundno aff
Anurani D. Persaud, Craig E. Williamson

Bibliographic record

VenueFreshwater Biology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPlanktonCopepodZooplanktonCladoceraEcologyBiologyFood webCrustaceanAbundance (ecology)Freshwater ecosystemEcosystemDaphniaTemperate climate

Abstract

fetched live from OpenAlex

Summary 1. Anthropogenic stressors such as climate change, ozone depletion and acidification may act in concert to alter ultraviolet (UV) light and temperature regimes in freshwater ecosystems. These physical and chemical changes will inevitably affect zooplankton community dynamics, but little is known about their relative effects on different species in natural communities. During spring, species that migrate to surface waters to take advantage of warmer temperatures may be especially vulnerable as UV levels can be high. 2. The objective of this study was to investigate the in situ effects of UV and temperature on a natural assemblage of planktonic rotifers and crustaceans during the spring. We performed in situ exposure experiments in two lakes with different surface temperatures. 3. Exposure to UV had a significant effect on the abundance and/or reproduction of four rotifers: Gastropus spp., Kellicottia bostonensis , Kellicottia longispina , Keratella spp.; two cladocerans: Holopedium gibberum , Daphnia catawba , and one copepod: Leptodiaptomus minutus . Incubation under cooler temperatures had a negative effect on K. longispina and H. gibberum . Temperature and UV had a significant interactive effect on abundance and/or reproduction of L. minutus and Ploesoma truncatum . Our results indicate that changes in underwater UV and temperature can significantly influence the composition of the zooplankton community and ultimately food web dynamics.

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 score0.089
Threshold uncertainty score0.927

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 head0.003
GPT teacher head0.199
Teacher spread0.195 · 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

Citations47
Published2005
Admission routes1
Has abstractyes

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