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Record W2106562261 · doi:10.2980/20-2-3584

Choice of foraging habitat by northern flickers reflects changes in availability of their ant prey linked to ambient temperature

2013· article· en· W2106562261 on OpenAlexafffundvenue
Karen L. Wiebe, Elizabeth A. Gow

Bibliographic record

VenueEcoscience · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Saskatchewan
FundersKenneth M. Molson FoundationSociety of Canadian Ornithologists
KeywordsForagingHabitatPredationEcologyGrasslandAbundance (ecology)WoodpeckerBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Foraging theory suggests animals should prefer habitats with a greater density of prey, but few have investigated whether birds change foraging habitats according to short-term changes in prey abundance caused by weather. We studied a woodpecker, the northern flicker (Colaptes auratus), in which the diet is composed mainly of ants collected on the ground surface. We measured the surface density of the ant prey in 1-m2 quadrats placed in 2 habitat types that had different thermal properties: open grassland and forest. The density of ants varied according to year of the study, habitat type, date during the summer, and time of day and was strongly associated with ambient temperature. In the shaded forest habitat, ant density increased linearly with air temperatures between 6 and 28 °C In contrast, the surface activity of ants in the open habitat exposed to sun began to decline once ground surface temperatures reached 26 °C Ant densities were higher in the open habitat than in the shade in relatively cold conditions but were higher in the shaded forest habitat when it was hot. Using radio telemetry, we recorded the habitat use of foraging flickers and found they shifted from foraging in the open when it was cold to foraging in shaded habitats when it was hot. Flickers tracked the density of their main prey on fine spatial and short temporal scales consistent with foraging theory.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.561

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2013
Admission routes3
Has abstractyes

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