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Record W1585149156 · doi:10.1002/jwmg.570

Band reporting probabilities for mallards recovered in the United States and Canada

2013· article· en· W1585149156 on OpenAlexaboutno aff
G. Scott Boomer, Guthrie S. Zimmerman, Nathan Zimpfer, Pamela R. Garrettson, Mark D. Koneff, Todd A. Sanders, Kimberly D. Magruder, J. Andrew Royle

Bibliographic record

VenueJournal of Wildlife Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsFlywayAnasWildlifeGeographyHunting seasonStatisticsEcologyDemographyBiologyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Reliable estimates of annual harvest rates are required for the implementation of mallard ( Anas platyrhynchos ) adaptive harvest management decision frameworks. Because not all standard bands recovered during the hunting season are reported, band reporting probabilities are needed to estimate mallard harvest rates. Information from birds recovered with bands that notify finders of a reward (i.e., reward bands) can be used to estimate band reporting rates. We analyzed reward banding data for 3 stocks of mallards to estimate reporting probabilities that can be used to estimate harvest rates for birds recovered with toll‐free or web‐address bands. Specifically, we explored spatial variability in reporting probabilities, and assessed whether reporting probabilities varied among years. Our analysis indicated that reporting probabilities varied among the 4 Flyways, eastern Canada, and western Canada and Alaska. We had difficulty interpreting temporal fluctuations and found little evidence for any meaningful trends in reporting rates between 2002 and 2010. We recommend that reporting probabilities of 0.67 in the Atlantic Flyway, 0.81 in the Mississippi Flyway, 0.70 in the Central Flyway, 0.76 in the Pacific Flyway, 0.50 in eastern Canada, and 0.57 in western Canada and Alaska be used to estimate harvest probabilities for birds recovered in these regions. © 2013 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.961

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 designNot applicable
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

Citations24
Published2013
Admission routes1
Has abstractyes

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