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Record W1963763431 · doi:10.1021/es0520619

Ecological Tracers Can Quantify Food Web Structure and Change

2006· article· en· W1963763431 on OpenAlexaff
Craig E. Hebert, Michael T. Arts, D. V. Chip Weseloh

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsTrophic levelFood webLarusHerringEcologyFood chainEcosystemHerring gullSeabirdEnvironmental scienceIsotope analysisδ15NStable isotope ratioFish <Actinopterygii>BiologyFisheryPredationδ13C

Abstract

fetched live from OpenAlex

Disruption of natural food webs is becoming a commonplace occurrence as a result of human activities. Considering this, there is a need to improve our ability to define food web structure as well as to detect and understand the implications of trophodynamic change. This requires the development, validation, and application of ecologicaltracers that can provide insights into the movement of energy, nutrients, and contaminants through food webs. In this study, we examine the utility of two groups of naturally occurring intrinsic tracers (stable nitrogen isotopes and fatty acids) to provide such information in a predatory seabird, the herring gull (Larus argentatus). Spatial and temporal patterns in gull trophic position (inferred from egg stable nitrogen isotope values) were related to gull diet composition (inferred from egg fatty acid concentrations). These two independent groups of ecological tracers provided corroborating evidence that gull trophic position was related to the degree to which aquatic foods, namely fish, were consumed. The use of these tracers in concert led to a better understanding of routes of energy flow and contaminant transfer in food webs and how these pathways may be affected by ecosystem change.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations83
Published2006
Admission routes1
Has abstractyes

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