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Record W1992205927 · doi:10.2193/2004-022

Wetland Selection by Mallard Broods in Canada's Prairie‐Parklands

2007· article· en· W1992205927 on OpenAlexafffundabout
Garnet Raven, Llwellyn M. Armstrong, David W. Howerter, Todd W. Arnold

Bibliographic record

VenueJournal of Wildlife Management · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDucks Unlimited Canada
FundersInstitute for Wetland and Waterfowl Research, Ducks Unlimited CanadaUniversity of Manitoba
KeywordsWetlandHabitatAnasEcologyBroodWaterfowlVegetation (pathology)PopulationGeographyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Although brood survival has a pronounced effect on population growth in mallards ( Anas platyrhynchos ), knowledge of brood ecology is more limited than for other vital rates. During 1993–1997 we collected wetland selection data from 210 radiomarked mallard broods on 15 study areas located throughout the Canadian Prairie‐Parklands. We used information‐theoretic approaches to select the best‐approximating model of habitat selection in relation to wetland characteristics. Wetland permanence, cover type, width of flooded emergent vegetation, and interactions between these variables and date, moisture level, and dominant species of emergent vegetation were all important predictors of wetland selection. Mallard broods selected deeper wetlands, especially later in the brood‐rearing season. Mallard broods also selected wetlands with large central expanses of open water and wide peripheral zones of flooded emergent cover. These habitat characteristics can most easily be met in landscapes that already contain an abundance and diversity of natural wetland habitats. Where such wetlands are unavailable, restoration or management of deeper wetlands may be necessary to meet the habitat requirements of mallard ducklings.

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 score0.684
Threshold uncertainty score0.907

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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

Citations19
Published2007
Admission routes3
Has abstractyes

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