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Record W2180936946 · doi:10.1139/cjz-2015-0067

Geophagic behavior in the mountain goat (<i>Oreamnos</i> <i>americanus</i>): support for meeting metabolic demands

2015· article· en· W2180936946 on OpenAlexvenueno aff
Brittany L. Slabach, Tyler B. Corey, June R. Aprille, Philip T. Starks, Benjamin Dane

Bibliographic record

VenueCanadian Journal of Zoology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPopulationEcologyObligateNutrientHabitatZoologyDemography

Abstract

fetched live from OpenAlex

Geophagy, the intentional consumption of earth or earth matter, occurs across taxa. Nutrient and mineral supplementation is most commonly cited to explain its adaptive benefits; yet many specific hypotheses exist. Previous research on mountain goats (Oreamnos americanus (Blainville, 1816)) broadly supports nutrient supplementation as the adaptive benefit of geophagy. Here, we use data from an undisturbed population of mountain goats inhabiting a geologically distinct coastal mountain range in southwestern British Columbia to test the hypothesis that geophagic behavior is a proximate mechanism for nutrient supplementation to meet metabolic demands. Our population, observed for over 30 consecutive years, returned each year with high fidelity to the same geophagic lick sites. Logistic regression demonstrated an overall effect of sodium and phosphorus, but not magnesium and calcium, on lick preferences. These data, in conjunction with field observations, provide support for the hypothesis that geophagy provides nutrient supplementation and that geophagy may be an obligate behavior to meet necessary metabolic demands within this population. The implications of our results suggest the necessity to preserve historically important habitats that may be necessary for population health.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.393
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2015
Admission routes1
Has abstractyes

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