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Record W2141452770 · doi:10.1644/13-mamm-a-133.1

Mapping the distribution of a prey resource: neonate caribou in Newfoundland

2014· article· en· W2141452770 on OpenAlexaffabout
Nathaniel D. Rayl, Todd K. Fuller, John F. Organ, John E. McDonald, Shane P. Mahoney, Colleen E. Soulliere, Steve E. Gullage, Tyler Hodder, Norman Frank, Truman S. Porter, Guillaume Bastille‐Rousseau, James A. Schaefer, Dennis L. Murray

Bibliographic record

VenueJournal of Mammalogy · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsTrent UniversityDepartment of Environment and ConservationGovernment of Newfoundland and Labrador
Fundersnot available
KeywordsHerdPredationIce calvingRange (aeronautics)EcologyPopulationGeographyBiologyHome rangePredatorHabitatDemographyLactation

Abstract

fetched live from OpenAlex

The recently diminished caribou (Rangifer tarandus) population in insular Newfoundland, Canada, has been severely limited by predation on newborn calves. These neonates are patchily distributed on the landscape; therefore, to adequately understand predator-prey interactions, the temporal and geographic extent of the distribution of caribou calves must be identified so that current areas with calves can be differentiated from areas without calves. We used telemetry locations of 309 caribou calves and 100 adult females from 4 herds, 2008–2010, to estimate the spatiotemporal distribution of caribou calves during the time when they were most vulnerable to predation, to evaluate the predictability of the calf resource among years, and to assess the degree of aggregation during calving. Patterns of calf distribution were predictable in time and space from year to year, with an average distributional overlap of 68% between years. The dispersion of female caribou during calving varied among herds from highly aggregated (8% and 20% of herd range) to more dispersed (50–70% of herd range). Postcalving (up to 9 weeks) distributions also varied among herds; the 2 more-dispersed herds remained sedentary, whereas both highly aggregated herds migrated away from their calving grounds at the end of June. The most-aggregated herd remained so as it migrated from its calving ground, whereas the less-aggregated herd spread out and moved in a variety of predictable directions. Dispersion and movement patterns varied with forest cover; herds with less forest cover in their range were more aggregated and migratory than herds with more forest cover.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.121

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)0.0000.000

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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