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Record W2133888553 · doi:10.2981/09-115

Evaluation of alternative management strategies of muskrat Ondatra zibethicus population control using a population model

2011· article· en· W2133888553 on OpenAlexaff
Daan Bos, Ron Ydenberg

Bibliographic record

VenueWildlife Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPopulationPopulation controlControl (management)Agency (philosophy)Management strategyIntegrated pest managementBusinessPopulation growthField (mathematics)Environmental resource managementOperations managementEcologyBiologyComputer scienceEngineeringEconomicsEnvironmental healthResearch methodologyMathematicsMedicine

Abstract

fetched live from OpenAlex

Abstract Muskrats Ondatra zibethicus are considered a pest species in the Netherlands, and a year‐round control programme is in effect. Currently, the agency responsible for the management of muskrat populations in the Netherlands (the LCCM) is preparing for field studies to compare alternative strategies of control. In order to decide on the specific design of such field studies, a population dynamic model was built. The model compares the current management strategy with alternatives in which the effort is focused in space or in time. The model allows us to prioritise future research questions. The major gaps in knowledge at this moment are: 1) insight into the costs of harvesting at different harvest rates, and 2) the relationship between population density on the one hand and (financial damage or) safety risk on the other hand. We suggest continuing the current management, and to test our hypothesis that intensifying harvest will lead to lower numbers of animals killed in the medium term than more extensive harvest rates. The muskrat control programme offers excellent opportunities for applied biological studies of which the benefits are likely to outweigh the costs.

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 score0.040
Threshold uncertainty score0.326

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 head0.112
GPT teacher head0.340
Teacher spread0.228 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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