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Record W2024111704

Interactions driving the population cycle of Arctic small rodents

2005· article· en· W2024111704 on OpenAlexaboutno aff
Lars Gårding

Bibliographic record

VenueLund University Publications (Lund University) · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulation cycleVolePredationArcticMicrotusPopulationEcologyPredatorAnnual cycleBiologySnowshoe hareDemography
DOInot available

Abstract

fetched live from OpenAlex

The cyclicity of Arctic populations of small rodents is a subject with a long history and a large literature (Batzli, 1992) in which the question "What drives the cycle?" has received many answers, among them that the source of the cycle is either rodent interaction with food or the interaction with predators or both. Another question concerns the confinement of the cycle to Arctic conditions. The paper by Garding (2000) presented a simple mathematical model of the combined predator-prey-food interaction based on a general eater-food interaction in which cycle length is an explicit decreasing function of the average birth rate of eaters. In the combined interaction, the cycle length is the same function of the sum of the average birth rates of predators and preys Numerical fits of these models make it possible to answer the questions above. The results are that the short 3-5 year cycles of the Arctic rodents: lemming (Lemmus lemmus) and vole (Microtus agrestis) are mainly driven by interaction with food while the ten year cycle of the Canadian snowshoe hare (Lepus americanus), is driven by interaction with its predator - lynx. Rodents in the Arctic live and breed in burrows and experience predation pressure when surfacing. This explains their interaction with food. The greater variety and easier availability of food in a temperate climate accounts for a missing rodent interaction with food. The paper starts with a presentation of the eater-food interaction model itself, its simple but unfamiliar mathematics and its points of credibility. At the end of the paper sonic current hypotheses about the nature of the rodent cycle are seen in the light of the model used here.

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 score0.497
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.208
Teacher spread0.195 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

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