MétaCan
Menu
Back to cohort
Record W2094135943 · doi:10.7557/2.23.5.1683

Caribou, military jets and noise: The interplay of behavioural ecology and evolutionary psychology

2003· article· en· W2094135943 on OpenAlexaffabout
Fred Harvey Harrington

Bibliographic record

VenueRangifer · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsWoodland caribouWoodlandEcologyPredationPopulationContext (archaeology)PredatorGeographyBiologyDemography

Abstract

fetched live from OpenAlex

Whether a human activity is likely to have a negative impact on a species depends largely on how stimuli from that activity are interpreted and acted upon by individuals, within the context of their behavioural ecology. The interpretations and decisions made by individuals in response to these stimuli are largely governed by neural systems evolved by the species as adaptations to common and recurrent selective pressures. In this paper I will review previous findings concerning the responses of caribou to overflights by military jet aircraft in Labrador/Quebec and Alaska, casting them in an evolutionary psychological framework. One prediction from such an exercise is that identical stimuli (noise from jet overflights) that elicit similar responses (short-distance avoidance) can have quite different population consequences for sedentary (woodland) and migratory (barren-ground) ecotypes. For a female woodland caribou, which shares her calving range with a resident predator population, an increase in movements following disturbance may significantly increase her calf's exposure to predators. Similar movements by a female barren-ground caribou, which has fewer predators to contend with, may have only a negligible impact on her calf's predation risk. Thus woodland caribou may be more vulnerable to negative impacts of military jet noise during calving periods, dependent on predator density.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

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)0.0010.000

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 head0.017
GPT teacher head0.264
Teacher spread0.248 · 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 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

Citations6
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueRangiferSame topicMarine animal studies overviewFrench-language works237,207