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Breeding-Season Survival of Male and Female Mallards in Canada's Prairie-Parklands

2006· article· en· W2174971625 on OpenAlexafffundabout
Michael G. Brasher, Todd W. Arnold, James H. Devries, Richard M. Kaminski

Bibliographic record

VenueJournal of Wildlife Management · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsDucks Unlimited Canada
FundersDelta WaterfowlInstitute for Wetland and Waterfowl Research, Ducks Unlimited Canada
KeywordsAnasBiologySeasonal breederNest (protein structural motif)PopulationHunting seasonEcologyAnimal scienceDemography

Abstract

fetched live from OpenAlex

Despite greater harvest rates of males, mallard (Anas platyrhynchos) populations exhibit male-biased sex ratios, presumably because females experience greater mortality during breeding seasons than do males. Nest success and adult female survival during the breeding season greatly impact mallard population growth; however, no study has evaluated breeding-season survival of males and females simultaneously using radiotelemetry. We captured, radiomarked, and monitored 90 male and 272 female mallards during 2 breeding seasons in the Prairie-Parkland region of Canada (Manitoba 1998, Saskatchewan 1999). Model-averaged estimates of female breeding season (i.e., 15 Apr–14 Jul) survival probabilities were 0.84 (SE = 0.031) in 1998 and 0.71 (SE = 0.040) in 1999. Estimated survival probabilities of paired males were 0.99 (SE = 0.016) in 1998 and 0.98 (SE = 0.025) in 1999; survival estimates for unpaired males were 0.92 (SE = 0.052) in 1998 and 0.85 (SE = 0.083) in 1999. Female mortality was greatest during periods of intensive nesting, whereas limited male mortalities precluded identification of attributes linked to mortality. Our results suggest that breeding-season survival of males has little impact on mallard population growth, and that management efforts to increase female survival rates offer greater potential to enhance mallard production.

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 score0.480
Threshold uncertainty score0.958

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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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

Citations36
Published2006
Admission routes3
Has abstractyes

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