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Record W4404556441 · doi:10.1002/ecs2.70072

Agricultural mosaics offer nesting habitat to dabbling ducks in the arid Intermountain West of the United States

2024· article· en· W4404556441 on OpenAlexfundno aff
Casey M. Setash, Adam C. Behney, James H. Gammonley, Liba Pejchar, David N. Koons

Bibliographic record

VenueEcosphere · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersInstitute for Wetland and Waterfowl Research, Ducks Unlimited CanadaColorado Water Conservation BoardColorado Parks and Wildlife
KeywordsNesting (process)HabitatAridGeographyAgricultureEcologyWaterfowlAgroforestryEnvironmental scienceBiologyArchaeology

Abstract

fetched live from OpenAlex

Abstract The debate over the best agricultural practices for biological conservation often focuses on the degree to which agricultural lands should be interspersed with desirable habitat versus protecting lands entirely from production. It is important to understand the benefits agriculture provides for wildlife because it is consuming an increasing proportion of the landscape. We evaluated the nesting ecology of breeding ducks within a mosaic of flood‐irrigated conservation areas and agricultural lands in hay production. We assessed how habitat features at two spatial scales across these lands were related to nest site selection, nest density, and nest survival of multiple duck species. Birds selected nest sites with higher visual obstruction, a higher proportion of shrubs around the nest, and less bare ground, but we did not detect evidence of selection per se at larger spatial scales. Nest density was marginally higher along linear features, including irrigation ditches and riparian stretches, but nest survival remained similar across land‐use types and habitats features. This system is representative of many agricultural landscapes around the globe and highlights the ways agroecosystems can be managed to maintain habitat suitability for wildlife on working lands.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.436

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 studies0.0000.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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