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Record W2063255847 · doi:10.1111/ele.12265

Effects of sea surface warming on marine plankton

2014· letter· en· W2063255847 on OpenAlexaff
Aleksandra M. Lewandowska, Daniel G. Boyce, Matthias Hofmann, Birte Matthiessen, Ulrich Sommer, Boris Worm

Bibliographic record

VenueEcology Letters · 2014
Typeletter
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsBedford Institute of OceanographyQueen's UniversityDalhousie University
Fundersnot available
KeywordsPhytoplanktonPlanktonZooplanktonEnvironmental scienceTrophic levelOceanographyBiomass (ecology)Global warmingEffects of global warming on oceansMesocosmMarine ecosystemEcologyEcosystemNutrientBiologyClimate changeGeology

Abstract

fetched live from OpenAlex

Ocean warming has been implicated in the observed decline of oceanic phytoplankton biomass. Some studies suggest a physical pathway of warming via stratification and nutrient flux, and others a biological effect on plankton metabolic rates; yet the relative strength and possible interaction of these mechanisms remains unknown. Here, we implement projections from a global circulation model in a mesocosm experiment to examine both mechanisms in a multi-trophic plankton community. Warming treatments had positive direct effects on phytoplankton biomass, but these were overcompensated by the negative effects of decreased nutrient flux. Zooplankton switched from phytoplankton to grazing on ciliates. These results contrast with previous experiments under nutrient-replete conditions, where warming indirectly reduced phytoplankton biomass via increased zooplankton grazing. We conclude that the effect of ocean warming on marine plankton depends on the nutrient regime, and provide a mechanistic basis for understanding global change in marine ecosystems.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.175
Teacher spread0.169 · 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 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

Citations270
Published2014
Admission routes1
Has abstractyes

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