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Record W2095725931 · doi:10.2980/17-1-3287

The potential of stable isotope (δ <sup>13</sup> C, δ <sup>15</sup> N) analyses for measuring foraging behaviour of animals in disturbed boreal forest

2010· article· en· W2095725931 on OpenAlexaffvenueabout
Amy F. Darling, Erin M. Bayne

Bibliographic record

VenueEcoscience · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsGolder Associates (Canada)University of Alberta
Fundersnot available
KeywordsPeromyscusMicrotusStable isotope ratioδ13CTaigaForagingBiologyPredationEcologyIsotopes of carbonδ15NIsotopePredatorIsotopes of nitrogenZoology

Abstract

fetched live from OpenAlex

We examined whether stable isotopes of carbon (δ13C) and nitrogen (δ15N) differed consistently between linear features (e.g., pipelines) and forest for plants, fungi, soil, small mammals, and arthropods in the southern Northwest Territories and northern Alberta, Canada. Overall, linear features were significantly enriched in 13C (+0.3%0) and depleted in 15N (-1.0%0) compared to forest. However, the small magnitude of the linear feature effect means isotope values probably cannot be used to directly trace whether insects or small mammals preferentially use linear features for foraging. However, deer mice (Peromyscus maniculatus) red-backed voles (Myodes spp.), and meadow voles (Microtus pennsylvanicus) differed significantly in stable isotope ratios. Each of these species also varied in their use of linear feature habitat, making it plausible to track where predators of small mammals obtain their prey using stable isotope ratios from predator tissues.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.106
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2010
Admission routes3
Has abstractyes

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