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Record W2766842118 · doi:10.1111/ele.12835

Surface water <scp>CO</scp><sub>2</sub> concentration influences phytoplankton production but not community composition across boreal lakes

2017· article· en· W2766842118 on OpenAlexaff
Richard J. Vogt, Nicolas Fortin St‐Gelais, Matthew J. Bogard, Beatrix E. Beisner, Paul A. del Giorgio

Bibliographic record

VenueEcology Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhytoplanktonBiomass (ecology)Environmental scienceEcologyBorealContext (archaeology)NutrientComposition (language)OceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Recent experimental evidence suggests that changes in the partial pressure of CO 2 ( pCO 2 ), in concert with nutrient fertilisation, may result in increased primary production and shifted phytoplankton community composition that favours species lacking adaptations to low CO 2 environments. It is not clear whether these results apply in ambient freshwaters, which are already often supersaturated in CO 2 , and where phytoplankton structure and activity are under complex control of diverse local and regional factors. Here, we use a large‐scale comparative study of 69 boreal lakes to explore the influence of existing CO 2 gradients ( c . 50–2300 μatm) on phytoplankton community composition and biomass production. While community composition did not respond to pCO 2 gradients, gross primary production was enhanced, but only in lakes already supersaturated in CO 2 , demonstrating that environmental context is key in determining pCO 2 –phytoplankton interactions. We further argue that increased atmospheric CO 2 is unlikely to influence phytoplanktonic composition and production in northern lakes.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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.

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

Citations39
Published2017
Admission routes1
Has abstractyes

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