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Record W2162836904 · doi:10.3996/032011-jfwm-023

Harvest Distribution and Derivation of Atlantic Flyway Canada Geese

2012· article· en· W2162836904 on OpenAlexaboutno aff
Jon D. Klimstra, Paul I. Padding

Bibliographic record

VenueJournal of Fish and Wildlife Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsFlywaySubarctic climatePopulationTemperate climateBayGeographyFisherySubsistence agricultureEcologyBiologyHabitatDemographyAgricultureArchaeology

Abstract

fetched live from OpenAlex

Abstract Harvest management of Canada geese Branta canadensis is complicated by the fact that temperate- and subarctic-breeding geese occur in many of the same areas during fall and winter hunting seasons. These populations cannot readily be distinguished, thereby complicating efforts to estimate population-specific harvest and evaluate harvest strategies. In the Atlantic Flyway, annual banding and population monitoring programs are in place for subarctic-breeding (North Atlantic Population, Southern James Bay Population, and Atlantic Population) and temperate-breeding (Atlantic Flyway Resident Population [AFRP]) Canada geese. We used a combination of direct band recoveries and estimated population sizes to determine the distribution and derivation of the harvest of those four populations during the 2004–2005 through 2008–2009 hunting seasons. Most AFRP geese were harvested during the special September season (42%) and regular season (54%) and were primarily taken in the state or province in which they were banded. Nearly all of the special season harvest was AFRP birds: 98% during September seasons and 89% during late seasons. The regular season harvest in Atlantic Flyway states was also primarily AFRP geese (62%), followed in importance by the Atlantic Population (33%). In contrast, harvest in eastern Canada consisted mainly of subarctic geese (42% Atlantic Population, 17% North Atlantic Population, and 6% Southern James Bay Population), with temperate-breeding geese making up the rest. Spring and summer harvest was difficult to characterize because band reporting rates for subsistence hunters are poorly understood; consequently, we were unable to determine the magnitude of subsistence harvest definitively. A better understanding of subsistence hunting is needed because this activity may account for a substantial proportion of the total harvest of subarctic populations. Our results indicate that special September and late seasons in the United States were highly effective in targeting AFRP geese without significantly increasing harvest of subarctic populations. However, it is evident that AFRP geese still are not being harvested at levels high enough to reduce their numbers to the breeding population goal of 700,000.

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 score0.029
Threshold uncertainty score0.661

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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