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How well are velocity effects on ∂<sup>13</sup>C signatures transmitted up the food web from algae to fish?

2009· article· en· W2150728460 on OpenAlexafffundabout
Joseph B. Rasmussen, Véronique Trudeau

Bibliographic record

VenueFreshwater Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMcGill UniversityUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeriphytonTrophic levelBenthic zoneFood webTributaryEnvironmental scienceAlgaeEcologyMacrophyteInvertebrateBiologyGeography

Abstract

fetched live from OpenAlex

Summary 1. Benthic algae fractionate carbon isotopes less at low water velocities because of reduced boundary layer exchange, and this effect on δ 13 C is passed on to consumers via trophic transfer. This study examines the relationships between δ 13 C signatures of consumers (invertebrates and salmonid fishes) and water velocity in the Sainte Marguerite River, QC, Canada, and compares them to patterns for periphyton, both along the river main‐stem and in a small tributary. 2. Relationships of δ 13 C signatures of herbivore/grazers and collector/gatherers with water velocity were strong and similar to those of periphyton, but relationships for filter‐feeders were weak, probably reflecting the effect of spatial averaging of their food supply as a result of downstream transport. 3. Velocity effects on salmonid signatures were much weaker than those of lower trophic levels, being barely significant except in the small tributary where the fish were resident and isolated from the main river. In the river main‐stem, even when reach standardised (reach mean subtracted from each data point), fish signatures were only weakly related to water velocity. 4. The fidelity with which velocity effects are transmitted to consumers from benthic algae is highly variable, and depends on a combination of consumer and resource movements, in addition to the trophic position of the consumer.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.202
Teacher spread0.196 · 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 designNot applicable
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

Citations12
Published2009
Admission routes3
Has abstractyes

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