MétaCan
Menu
Back to cohort
Record W2587495484

2013-2014 Illinois Waterfowl Hunter Report: Harvest, Season Preferences, and Digest Use

2014· article· en· W2587495484 on OpenAlexaboutno aff
Andrew L. Stephenson, Brent D. Williams, Linda K. Campbell, Craig A. Miller

Bibliographic record

VenueIDEALS (University of Illinois Urbana-Champaign) · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServiceIllinois Department of Natural Resources
KeywordsWaterfowlGeographyFisheryEcologyBiologyHabitat
DOInot available

Abstract

fetched live from OpenAlex

A total of 3,278(46%response rate) Illinois waterfowl hunters responded to the 2013-14Illinois Waterfowl Hunter Survey. An estimated 49,170 waterfowl hunters spent1,052,728 days afield, adecreaseof 8.9% from the 1,155,346 days devoted during the 2012-2013 license year. Waterfowl harvest increased 4.3%, from 580,557 during 2012-13to 605,720 during 2013-14.Duck harvest estimates for the regular duck season were as follows: 225,873 mallards (Anas platyrhynchos), 49,001wood ducks (Aix sponsa), and 155,306 other ducks. A total of 21,967 teal (Anasspp.) were harvested during the September teal season. Goose hunters harvested 104,887Canada geese (Branta canadensis) during the regular Canada goose season, a 44.3% increase from the 72,682 Canadageese harvested during the 2012-13regular goose season. Hunters harvested 15,644 Canada geese during the September Canada goose season, a 13.2% decrease from the previous year. During the Youth Waterfowl Hunting Season, 8,438 adults took 8,639 youths waterfowl hunting, a 7.8% increase in adult participation and a 13.6% decrease in youth participation from the 2012-13 Youth Waterfowl Hunting Season.Duck hunterpreference for season dates and zones,satisfaction with the waterfowl seasons, and record keeping are also discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.190
Teacher spread0.177 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIDEALS (University of Illinois Urbana-Champaign)Same topicAvian ecology and behaviorFrench-language works237,207