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Record W2116281145 · doi:10.1139/cjz-2012-0103

Dietary segregation between two cohabiting species of sparrows revealed with stable isotope analysis

2012· article· en· W2116281145 on OpenAlexafffundvenueabout
J. Mark Hipfner, BriAnne Addison, Mathieu R. Charette

Bibliographic record

VenueCanadian Journal of Zoology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsCanadian Society of Intestinal ResearchEnvironment and Climate Change Canada
FundersSimon Fraser University
KeywordsSparrowBiologyHabitatTrophic levelEcologyPredationIsotope analysisZoology

Abstract

fetched live from OpenAlex

Fox Sparrows (Passerella iliaca (Merrem, 1786)) and Song Sparrows (Melospiza melodia (A. Wilson, 1810)) cohabit on many islands along the Pacific coast of North America, and previous studies suggest that they rely on similar prey types. We used δ13C and δ15N stable isotope analysis on blood collected from breeding adults of both species in each of two habitats on Triangle Island, British Columbia, Canada, to test the hypothesis that the two species exhibit a consistent pattern (direction) of dietary segregation in different habitat types. Both δ13C and especially δ15N values differed between habitats, indicating that the two habitats were isotopically distinct. As predicted, δ15N values differed consistently between the two species in the two habitats, averaging ∼1.5‰ higher in the smaller Song Sparrow than in the larger Fox Sparrow in both. We infer that Song Sparrows included more animal matter and less plant matter in their diets than Fox Sparrows, and suggest that fixed traits related to body size might underlie the dietary (trophic) differences. In contrast, δ13C values did not differ between species. We conclude that dietary segregation could help to facilitate the widespread cohabitation of these two species of sparrows.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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

Citations2
Published2012
Admission routes4
Has abstractyes

Explore more

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