MétaCan
Menu
Back to cohort
Record W1972689401 · doi:10.1139/cjz-2014-0237

Effects of geophagy on food intake, body mass, and nutrient dynamics of snowshoe hares (<i>Lepus</i> <i>americanus</i>)

2015· article· en· W1972689401 on OpenAlexfundvenueno aff
Suzanne Worker, Knut Kielland, Perry S. Barboza

Bibliographic record

VenueCanadian Journal of Zoology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
FundersNational Park ServiceParks Canada
KeywordsWillowBiologySnowshoe hareForageHerbivoreGeneralist and specialist speciesNutrientAnimal scienceIngestionAgronomyEcologyHabitat

Abstract

fetched live from OpenAlex

Geophagy is thought to have several physiological benefits to animals, including mineral supplementation and adsorption of toxins. However, these benefits have rarely been demonstrated experimentally. Using soil from a known lick in northern Alaska, we investigated the effect of geophagy on food intake and mass loss in captive snowshoe hares (Lepus americanus Erxleben, 1777) fed a formulated ration of feltleaf willow leaves (Salix alaxensis (Andersson) Coville), a preferred forage of snowshoe hares. Lick soil contained 4.8% clay, 12.5% carbonates, and had more available Na (72.7 mg·kg −1 ) than willow leaves. Hares fed willow leaves consumed more soil (15.2 ± 2.0 g·kg –0.75 ·d −1 ) than those fed formulated ration (9.3 ± 1.3 g·kg –0.75 ·d −1 ). Hares had higher willow intake when offered soil (45.8 ± 2.0 g·kg –0.75 ·d −1 ) than those without soil (35.0 ± 2.1 g·kg –0.75 ·d −1 ), and willow-fed hares offered soil lost less body mass (P &lt; 0.001) than those without soil. Soil availability resulted in higher digestible intakes of N, Ca, Mg, Na, and K, but not P. These results suggest that the availability of mineral licks may alter forage use and functional response of this generalist herbivore in interior Alaska.

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)0.001
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 score0.115
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of ZoologySame topicTherapeutic Uses of Natural ElementsFrench-language works237,207