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Record W2135930202 · doi:10.4319/lo.2012.57.5.1389

Regional‐scale effects of eutrophication on ecosystem structure and services of seagrass beds

2012· article· en· W2135930202 on OpenAlexafffundabout
Allison L. Schmidt, Jessica K. C. Wysmyk, Susanne E. Craig, Heike K. Lotze

Bibliographic record

VenueLimnology and Oceanography · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsEutrophicationSeagrassEnvironmental scienceZostera marinaBiomass (ecology)Benthic zonePhytoplanktonEpiphyteEcologyEcosystemEstuaryOceanographyNutrientBiology

Abstract

fetched live from OpenAlex

Using large‐scale field surveys across 12 estuaries in two provinces in Atlantic Canada, we analyzed changes in phytoplankton and benthic macroalgal communities as well as the canopy structure of eelgrass beds and quantified their carbon and nitrogen storage with increasing eutrophication. As eutrophication increased, phytoplankton biomass increased on average 1.8 times and phaeopigments doubled. Among macroalgae, the epiphytic Ulothrix speciosa increased 40 times in New Brunswick, and benthic Ulva lactuca 670 times in Prince Edward Island covering 61% of the bottom. Eelgrass showed a significant increase in leaf length and declines in shoot density and aboveground and belowground biomass, consistent with increased shading by opportunistic algae. As eelgrass biomass declined, so did the carbon storage capacity of the habitat. Nitrogen storage only declined in belowground eelgrass beds due to increasing tissue nitrogen content above ground with eutrophication. Despite province‐ and species‐specific responses of primary producers to nutrient loading, principal component analysis revealed an overall shift from perennial eelgrass to opportunistic macroalgae and phytoplankton with eutrophication at the regional scale, indicating generalized eutrophication effects on primary producer assemblages.

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 score0.013
Threshold uncertainty score0.246

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 head0.004
GPT teacher head0.173
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 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

Citations95
Published2012
Admission routes3
Has abstractyes

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