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Record W4403755627 · doi:10.1002/lol2.10445

Disentangling effects of droughts and heatwaves on alpine periphyton communities: A mesocosm experiment

2024· article· en· W4403755627 on OpenAlexafffund
Tamika L. Nagao, Rolf D. Vinebrooke

Bibliographic record

VenueLimnology and Oceanography Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Alberta
FundersAustralasian Gynaecological Endoscopy and Surgery SocietyNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPeriphytonMesocosmEnvironmental scienceClimate extremesGeographyEcologyPhysical geographyClimate changeEcosystemBiologyBiomass (ecology)

Abstract

fetched live from OpenAlex

Abstract The accelerating rate of global climate change at higher elevations and latitudes is increasing the potential for extreme climatic events. Here, a knowledge gap exists in how the order of exposure to, and duration of droughts and heatwaves affect their cumulative impact on aquatic communities. We tested experimentally for the legacy effects of simultaneous vs. sequential exposures to drought and heatwave on sediment‐dwelling algal communities (epipelon) from small fishless alpine lakes. In both simultaneous, and sequential exposure treatments involving drought followed by a heatwave, the negative effect of drought masked the effects of warming on chlorophyll‐inferred algal biomass and taxonomic composition. Reversal of order of exposure (i.e., heatwave followed by drought) lowered their cumulative effect on community structure. These findings highlight the potential for drought events to dominate over heatwaves in altering shallow littoral ecosystems at high elevations under a rapidly warming climate.

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 score0.055
Threshold uncertainty score0.482

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 head0.004
GPT teacher head0.206
Teacher spread0.202 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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