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Record W2128987355 · doi:10.1890/13-1586.1

Terrestrial carbon is a resource, but not a subsidy, for lake zooplankton

2013· article· en· W2128987355 on OpenAlexaff
Patrick T. Kelly, Christopher T. Solomon, Brian C. Weidel, Stuart E. Jones

Bibliographic record

VenueEcology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsZooplanktonEnvironmental scienceProductivityEcologyResource (disambiguation)HabitatFood webEcosystemAquatic ecosystemBiology

Abstract

fetched live from OpenAlex

Inputs of terrestrial organic carbon (t-OC) into lakes are often considered a resource subsidy for aquatic consumer production. Although there is evidence that terrestrial carbon can be incorporated into the tissues of aquatic consumers, its ability to enhance consumer production has been debated. Our research aims to evaluate the net effect of t-OC input on zooplankton. We used a survey of zooplankton production and resource use in ten lakes along a naturally occurring gradient of t-OC concentration to address these questions. Total and group-specific zooplankton production was negatively related to t-OC. Residual variation in zooplankton production that was not explained by t-OC was negatively related to terrestrial resource use (allochthony) by zooplankton. These results challenge the designation of terrestrial carbon as a resource subsidy; rather, the negative effect of reduced light penetration on the amount of suitable habitat and the low resource quality of t-OC appear to diminish zooplankton production. Our findings suggest that ongoing continental-scale increases in t-OC concentrations of lakes will likely have negative impacts on the productivity of aquatic food webs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

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)0.0060.001

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

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

Citations131
Published2013
Admission routes1
Has abstractyes

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