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Record W2889321974 · doi:10.1111/2041-210x.13081

The Data‐Limited Methods Toolkit (<scp>DLM</scp>tool): An R package for informing management of data‐limited populations

2018· article· en· W2889321974 on OpenAlexafffund
Thomas R. Carruthers, Adrian Hordyk

Bibliographic record

VenueMethods in Ecology and Evolution · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaCalifornia Department of Fish and WildlifeResources Legacy FundGordon and Betty Moore Foundation
KeywordsR packageComputer scienceData managementPopulationData mining

Abstract

fetched live from OpenAlex

Abstract A simulation‐based approach known as management strategy evaluation ( MSE ) is increasingly used by resource managers to identify management procedures that are robust to uncertainties in system dynamics. The majority of global fish populations are data limited and there is large uncertainty over their population and exploitation dynamics. The Data‐Limited Methods Toolkit (DLMtool) is an R package that allows for rapid and flexible MSE specification. The package consolidates a large number of existing data‐limited management procedures and allows for rapid MSE testing of new approaches. The DLMtool package has supported transparent and rigorous decision‐making for a number of data‐limited populations, identifying robust management procedures and revealing performance trade‐offs.

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)0.009
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.253
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.104
GPT teacher head0.413
Teacher spread0.309 · 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
GenreMethods

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

Citations56
Published2018
Admission routes2
Has abstractyes

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