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Record W2113291400 · doi:10.1002/jwmg.252

Food resource availability for American black ducks wintering in southern New Jersey

2011· article· en· W2113291400 on OpenAlexaff
Dane M. Cramer, Paul M. Castelli, Tina Yerkes, Christopher K. Williams

Bibliographic record

VenueJournal of Wildlife Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsDucks Unlimited Canada
Fundersnot available
KeywordsHabitatWaterfowlAnasWildlifeEcologyOverwinteringWildlife refugeResource (disambiguation)GeographyHabitat conservationFisheryBiology

Abstract

fetched live from OpenAlex

Abstract Midwinter waterfowl survey data indicates a long‐term decline in the number of wintering American black ducks ( Anas rubripes ), potentially due to habitat limitations. In order for future estimates of carrying capacity to be determined, it is critical that regional food availability is estimated. We collected pairs of habitat core samples ( n = 510) from 5 habitat types in southern New Jersey, USA, during October, January, and April 2006–2008 to estimate resource availability and variability. We collected upper gastrointestinal tracts from hunter‐killed birds ( n = 45) and late season collections ( n = 19) to identify food items and limited our estimates of resource availability to only winter food items; thereby reducing the availability of seed foods found in our core samples by 38% and animal foods by 96%. We did not detect differences in years or sampling period, but did between habitat types. Mudflat habitat had the greatest availability of invertebrate and vertebrate food items and appeared to supply the bulk of energy to black ducks wintering in southern New Jersey. We suggest conservation efforts to be focused on restoring or enhancing mudflat habitat as an integral component of an ecologically functioning salt marsh to increase food availability. © 2011 The Wildlife Society.

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 score0.037
Threshold uncertainty score0.899

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)0.0010.000

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 head0.028
GPT teacher head0.236
Teacher spread0.209 · 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

Citations34
Published2011
Admission routes1
Has abstractyes

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