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NEST SURVIVAL OF SCAUP AND OTHER DUCKS IN THE BOREAL FOREST OF ALASKA

2005· article· en· W2180780515 on OpenAlexaboutno aff
Johann Walker, Mark S. Lindberg, Margaret C. MacCluskie, Michael J. Petrula, James S. Sedinger

Bibliographic record

VenueJournal of Wildlife Management · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsAythyaNest (protein structural motif)WaterfowlPredationHabitatEcologyFisheryBiologyGeography

Abstract

fetched live from OpenAlex

We estimated variation in nest survival of lesser scaup (Aythya affinis), greater scaup (A. marila), and other common duck species at Minto Flats, Alaska, USA, during 1989–1993 and 2002–2003. Daily survival probability of scaup nests, as well as nests of all other duck species, varied with year, date, and nest habitat. Daily survival probability was unrelated to nest age and distance from the nest to water. Average, year-specific nest survival of all ducks at Minto Flats was 0.11 (95% CI: 0.05 to 0.22), comparable to nest survival of ducks breeding in mid-continent regions (i.e., the prairie pothole region and the Canadian prairie-parklands). Nest survival of scaup was variable among years, ranging from 0.01 (95% CI: 0.00 to 0.06) in 1992 to 0.61 (95% CI: 0.50 to 0.74) in 1993 and was probably related to variation in predation risk and water levels. Scaup production could have been limited by low nest survival during most years of this study; nest survival exceeded 20% only in 1993 and 2002. Because of the high densities of breeding scaup and other waterfowl species at Minto Flats, we recommend management to maintain existing habitat for breeding scaup and other duck species. This management could be most effectively informed by yearly monitoring of production to more accurately understand spatial and temporal variation in recruitment and to identify potential effects of proposed oil and gas exploration on recruitment of ducks at Minto Flats.

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 score0.009
Threshold uncertainty score0.173

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 head0.012
GPT teacher head0.242
Teacher spread0.229 · 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

Citations42
Published2005
Admission routes1
Has abstractyes

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