MétaCan
Menu
Back to cohort
Record W2044578770 · doi:10.1650/condor-14-45.1

Legacy effects of habitat degradation by Lesser Snow Geese on nesting Savannah Sparrows

2014· article· en· W2044578770 on OpenAlexaboutno aff
Stephen L. Peterson, Robert F. Rockwell, Christopher R. Witte, David N. Koons

Bibliographic record

VenueOrnithological Applications · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyHabitatSparrowAnatidaeHabitat destructionGooseSnowGeographyPopulationBiology

Abstract

fetched live from OpenAlex

Increased growth of the midcontinental population of Lesser Snow Geese (Chen caerulescens caerulescens) has led to overgrazing and habitat degradation at their Arctic and sub-Arctic breeding grounds. This habitat degradation has been shown to induce a trophic cascade that negatively affects plant, insect, and other avian species that share these habitats. In conjunction with a long-term study of the impacts of Lesser Snow Geese on habitat, we examined the dual influences of climate and long-term habitat change on the nesting occurrence of Savannah Sparrows near Churchill, Manitoba, Canada. Using multistate occupancy models, we found that variability in early-summer temperature and precipitation modulated year-to-year variability in nesting occurrence and detection probabilities. Extreme warm and wet conditions in early summer can benefit breeding Savannah Sparrows across the landscape. However, such events have not been prevalent enough to override the ~80% decline in Savannah Sparrow nesting occurrence over 36 yr. This dramatic decline can be attributed to the legacy of Lesser Snow Goose foraging, which has led to an 84% reduction in preferred shrub habitat for nesting Savannah Sparrows. Management actions targeted at reducing Lesser Snow Goose abundance and habitat restoration will be needed to allow sympatric Savannah Sparrows and functionally similar species to recover.

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 score0.473
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueOrnithological ApplicationsSame topicClimate variability and modelsFrench-language works237,207