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Record W2098170605 · doi:10.1139/cjz-2013-0263

Greater Sage-Grouse (<i>Centrocercus</i><i>urophasianus</i>) hen survival: effects of raptors, anthropogenic and landscape features, and hen behavior

2014· article· en· W2098170605 on OpenAlexvenueno aff
Jonathan B. Dinkins, Michael R. Conover, Christopher P. Kirol, Jeffrey L. Beck, S. Nicole Frey

Bibliographic record

VenueCanadian Journal of Zoology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPredationFlockHabitatEcologyPopulationZoologyDemography

Abstract

fetched live from OpenAlex

Survival of breeding-age hens has been identified as the demographic rate with the greatest potential to influence population growth of Greater Sage-Grouse (Centrocercus urophasianus (Bonaparte, 1827); hereafter “Sage-Grouse”). During 2008–2011, we collected summer survival data from 427 Sage-Grouse hens in southern Wyoming, USA. We assessed the effects of raptor densities, anthropogenic features, landscape features, and Sage-Grouse hen behavior on Sage-Grouse hen survival. Survival of Sage-Grouse hens was positively associated with the proportion of big sagebrush (genus Artemisia L.) habitat within 0.27 km radius and road density and negatively associated with power-line density, proximity to forested habitat, and topographic ruggedness index within 0.27 km radius (TRI 0.27 ). Raptor densities did not have individual effects on Sage-Grouse survival; however, an interaction between site-specific exposure to Golden Eagle (Aquila chrysaetos (L., 1758)) density (GOEA) and TRI 0.27 indicated that negative effects of GOEA and TRI 0.27 were dampened in areas with both high TRI 0.27 and high GOEA. Survival of nonreproductive hens was greater than brooding or nesting hens. Hens that stayed in intermediate-size flocks and yearling hens had higher survival than hens in small or large flocks and hens &gt;2 years old. Results indicated that risk of death for Sage-Grouse hens was greater relative to potential raptor perches but not anthropogenic and landscape variables that could provide food subsidies for predators.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.477

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)0.0000.000

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Citations44
Published2014
Admission routes1
Has abstractyes

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