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Two mesocosm experiments investigating the control of summer phytoplankton growth in a small shallow lake

2004· article· en· W2105856212 on OpenAlexfundno aff
Deborah Stephen, D. M. Balayla, S. E. Collings, Brian Moss

Bibliographic record

VenueFreshwater Biology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
FundersEuropean CommissionMcGill University
KeywordsPhytoplanktonMesocosmMacrophyteBiomanipulationZooplanktonBiomass (ecology)Dominance (genetics)NutrientEnvironmental scienceEcologyPlanktonEutrophicationPeriphytonChlorophyll aPlanktivoreBiologyBotany

Abstract

fetched live from OpenAlex

Summary 1. Mesocosm experiments were carried out to examine the relative importance of top down (fish predation) and bottom up (nutrient addition) controls on phytoplankton abundance in a small shallow lake, Little Mere, U.K., in 1998 and 1999. These experiments were part of a series at six sites across Europe. 2. In the 1998 experiment, top‐down processes (through grazing of large Cladocera) were important in determining phytoplankton biomass. The lack of plant refugia for zooplankton was probably important in causing an increasing chlorophyll a concentration even at intermediate fish density. Little Mere normally has abundant macrophytes but they failed to develop substantially during both years. Bottom‐up control was not important in 1998, most probably because of high background nutrient concentrations, as a result of nutrient release from the sediments. 3. In 1999 neither top‐down nor bottom‐up processes were significant in determining phytoplankton biomass. Large cladoceran grazers were absent even in the fish‐free enclosures, probably because dominance of cyanobacteria and high phytoplankton biomass made feeding conditions unsuitable. As in 1998, bottom‐up control of phytoplankton was not important, owing to background nutrient concentrations that were even higher in 1999 than in 1998, perhaps because of the warmer, sunnier weather. 4. The differing outcomes of the two experiments in the same lake with similar experimental designs highlight the importance of starting conditions. These conditions in turn depended on overall weather conditions prior to the experiments.

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 score0.428
Threshold uncertainty score0.909

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 head0.020
GPT teacher head0.248
Teacher spread0.227 · 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

Citations33
Published2004
Admission routes1
Has abstractyes

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