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Record W2052797178 · doi:10.1139/z08-023

Discrimination of intra- and inter-specific forage quality by collared pikas (Ochotona collaris)

2008· article· en· W2052797178 on OpenAlexafffundvenue
Shawn F. Morrison, David S. Hik

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsArctic Institute of North America
KeywordsBiologyForageForagingHerbivoreSelection (genetic algorithm)EcologyZoology

Abstract

fetched live from OpenAlex

The specific nutritional characteristics by which herbivores evaluate their foraging options are complex. We experimentally manipulated the crude protein and water content of two forage species ( Carex consimilis Holm. (= Carex bigelowii Torr. ex Schwein.) and Polygonum bistorta L.) commonly cached by collared pikas ( Ochotona collaris (Nelson, 1893)) to determine their influence on inter- and intra-specific forage selection. Preference data were collected for 27 pikas using cafeteria-style feeding trials in a randomized block design. A three-way interaction (species × protein × water) suggested that pikas made conditional forage selection decisions while caching these plants. The interaction was driven by greater selection for fresh rather than dried C. consimilis when both were not fertilized. Water content had no effect on the selection of either fertilized C. consimilis or fertilized P. bistorta. Overall, our results indicate that pikas made subtle decisions about their selection of vegetation during caching, based on variation in nitrogen and water content in addition to species-specific selection criteria. Further, our results imply that tests of foraging theory may need to consider intra-specific variation in forage characteristics, as well as inter-specific ranking of forage species.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.228
Teacher spread0.187 · 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 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

Citations14
Published2008
Admission routes3
Has abstractyes

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