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Record W4391490508 · doi:10.1017/9781805430551.026

Wolf–Beaver Dynamics in the Greater Voyageurs Ecosystem, Minnesota

2023· other· en· W4391490508 on OpenAlexaboutno aff
Thomas D. Gable, Sean Johnson‐Bice, Austin T. Homkes, Steve K. Windels, John G. Bruggink, Joseph K. Bump

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsBeaverGeographyDynamics (music)EcosystemEcologyBiologyPsychology

Abstract

fetched live from OpenAlex

In the heart of the boreal forest in 1949, trappers gathered at a spring meeting in Wabowden, Manitoba, to discuss many items of business, including wolf predation on beavers. Recent debate and disagreement had broken out among the trappers regarding whether wolves actually killed beavers. One trapper stated wolves ‘harassed’ a beaver colony so extensively that he had to fell trees into the water to ensure the colony's survival. Some trappers remained sceptical and unconvinced. The debate was put to a lively and emphatic end when a trapper walked into the spring meeting and presented a bushel sack stuffed with wolf scats containing beaver fur (Nash 1951). The proof was in the poop! Surprisingly, our understanding of wolf predation on beavers has progressed relatively little since 1949. Most attempts to study wolf predation on beavers followed an approach akin to the Manitoba trappers: collecting and examining wolf scats. By doing this, researchers in many areas across North America and Eurasia concluded, like the trappers, that beavers were important prey for wolves during the ice-free season. However, wolf–beaver dynamics received little attention beyond this, largely because (1) most wolf predation research was focused on wolf–ungulate interactions and predation on smaller alternate prey was not a priority (Gable et al 2018c), and (2) rigorously studying wolf predation during spring to autumn in forested ecosystems with dense vegetation was a monumental, and often impossible, task prior to GPS collar technology. Of course, many researchers and biologists had interesting ideas or hypotheses about wolf–beaver interactions, but most were based on anecdotal observations, indirect evidence or conjecture (Gable et al 2018c). None the less, these ideas were compelling and relevant. Some suggested dense beaver populations increased wolf pup survival (Benson et al 2013) and, in turn, wolf pack and population size (Andersone 1999; Barber-Meyer et al 2016). Others posited that dense beaver populations reduced wolf predation on ungulate prey (Forbes and Theberge 1996) while some claimed it increased predation (Andersone and Ozoliņš 2004; Latham et al 2013). Still others suspected wolves changed ecosystems by altering the ecosystem engineering behaviour of beavers (Peterson et al 2014). Clearly, wolf–beaver dynamics needed to be studied in more detail.

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)0.000
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 score0.491
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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