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Record W4205538980 · doi:10.1139/cjz-2021-0162

What do caribou eat? A review of the literature on caribou diet

2022· review· en· W4205538980 on OpenAlexafffundvenue
Quinn M. R. Webber, Kristy M. Ferraro, Jack G. Hendrix, Eric Vander Wal

Bibliographic record

VenueCanadian Journal of Zoology · 2022
Typereview
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsCircumpolar starLichenBiologyArcticContext (archaeology)EcologyWoodland caribouPredationBoreal

Abstract

fetched live from OpenAlex

Historically, the study of caribou and reindeer (Rangifer tarandus (Linnaeus, 1788)) diet has been specific to herds and few comprehensive circumpolar analyses of Rangifer diet exist. As a result, certain diet items may play an outsized role in the caribou diet Zeitgeist, e.g., lichen. We challenge this notion and test the relevant importance of various diet items within the context of prevailing hypotheses. We provide a systematic overview of 30 caribou studies reporting caribou diet and test biologically relevant hypotheses about spatial and temporal dietary variation. Our results indicate that in the winter caribou primarily consume lichen, but in warmer seasons and when primary productivity is lower, caribou primarily consume graminoids and other vascular plants. In more productive environments, where caribou have more competitors and predators, consumption of lichen increase. Overall, our description of caribou diet reveals that it is highly variable, but in circumstances where caribou can consume vascular plants, then they will. As climate change affects Boreal and Arctic ecosystems, the type and volume of food consumed by caribou have become an increasingly important focus for conservation and management of caribou.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.568
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.385
Teacher spread0.324 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations61
Published2022
Admission routes3
Has abstractyes

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