MétaCan
Menu
Back to cohort
Record W2143633121 · doi:10.1890/11-1595.1

Warming modifies trophic cascades and eutrophication in experimental freshwater communities

2012· article· en· W2143633121 on OpenAlexaff
Pavel Kratina, Hamish S. Greig, Patrick L. Thompson, Ticiana S. A. Carvalho‐Pereira, Jonathan B. Shurin

Bibliographic record

VenueEcology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill UniversityUniversity of British Columbia
FundersRural Development Administration
KeywordsEutrophicationTrophic levelTrophic cascadeEnvironmental scienceMesocosmBiomass (ecology)PhytoplanktonEcosystemEcologyGlobal warmingProductivityFreshwater ecosystemClimate changeNutrientFood webBiology

Abstract

fetched live from OpenAlex

Climate warming is occurring in concert with other anthropogenic changes to ecosystems. However, it is unknown whether and how warming alters the importance of top-down vs. bottom-up control over community productivity and variability. We performed a 16-month factorial experimental manipulation of warming, nutrient enrichment, and predator presence in replicated freshwater pond mesocosms to test their independent and interactive impacts. Warming strengthened trophic cascades from fish to primary producers, and it decreased the impact of eutrophication on the mean and temporal variation of phytoplankton biomass. These impacts varied seasonally, with higher temperatures leading to stronger trophic cascades in winter and weaker algae blooms under eutrophication in summer. Our results suggest that higher temperatures may shift the control of primary production in freshwater ponds toward stronger top-down and weaker bottom-up effects. The dampened temporal variability of algal biomass under eutrophication at higher temperatures suggests that warming may stabilize some ecosystem processes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations294
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueEcologySame topicFish Ecology and Management StudiesFrench-language works237,207