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Record W2126569345 · doi:10.1002/jwmg.118

Are feeding preferences of white‐tailed deer related to plant constituents?

2011· article· en· W2126569345 on OpenAlexafffund
Simon Dostaler, Jean‐Pierre Ouellet, Jean‐François Therrien, Steeve D. Côté

Bibliographic record

VenueJournal of Wildlife Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité LavalCenter for Northern StudiesUniversité du Québec à Rimouski
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsOdocoileusHerbivoreBiologyWildlifeForagingEcologyAgronomyAnimal science

Abstract

fetched live from OpenAlex

Abstract Controlled feeding experiments can provide valuable insights into food selection of herbivores. We conducted cafeteria trials on captive yearling white‐tailed deer ( Odocoileus virginianus ) during 2 years to determine feeding preferences in relation to plant chemical constituents, i.e., nitrogen and fibers. We simultaneously offered 8 species of cultivated and wild plants in monthly foraging trials conducted from June to October. We predicted that species preferences would be positively related to protein content from June to August and to digestible energy in September and October. As predicted, crude protein (CP) was positively related to feeding preferences, particularly as summer progressed. Feeding preferences were also negatively related to fiber content, especially in early summer. Our results indicate high protein needs over the complete growing season for yearling deer but a decrease in overall plant selectivity as summer progresses. Our results also suggest that deer browsing on cultivated plants might be due to higher CP content of cultivated plants than wild plants. To prevent deer impact on crops, managers should favor regeneration of plants rich in CP content in forests. © 2011 The Wildlife Society.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.024
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.027
GPT teacher head0.220
Teacher spread0.193 · 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

Citations55
Published2011
Admission routes2
Has abstractyes

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