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Record W7048344233

Landscape-level effects of agricultural intensification on the condition and diet of nestling Barn Swallows (<em>Hirundo rustica</em>)

2018· article· en· W7048344233 on OpenAlexfundaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersEnvironment and Climate Change Canada
KeywordsBarnInsectivorePredationAgricultureFirewood
DOInot available

Abstract

fetched live from OpenAlex

Farmland bird populations have experienced declines with increasing agricultural intensification for which the leading hypothesis is a reduction of prey insects. This may be especially relevant for aerial insectivores whose primary diet is aerial insects. For this thesis, I examined nestling body condition and used stable isotopes (δ13C, δ15N) and fecal DNA barcoding to determine the diet of a farmland breeding aerial insectivore, the Barn Swallow (Hirundo rustica), within an agro-ecosystem in Southern Ontario, Canada. Nestling body condition was positively affected by agricultural intensification, but all benefits were lost by the pre-fledging stage and with no effect on productivity. Stable isotopes indicated that nestling diet was derived from within agro-ecosystems. While nestling diet breadth was negatively affected by agricultural intensification, I found evidence for a robust dipteran diet unaffected by landscape. My results provide little evidence of long-term negative repercussions to breeding within agriculturally intense landscapes for the Barn Swallow.

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)0.000
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 score0.084
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.047
GPT teacher head0.280
Teacher spread0.234 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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