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Record W4390462080 · doi:10.1672/ucrt083-253

Assisting Nature: Ducks, “Ding” and DU by Arnold van der Valk

2018· article· en· W4390462080 on OpenAlexaboutno aff
Arnold van der Valk

Bibliographic record

VenueWetland Science and Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsWaterfowlWildlifeNewspaperFoundation (evidence)Environmental ethicsPolitical scienceGeographyHabitatEcologyArchaeologyLawBiology

Abstract

fetched live from OpenAlex

J ay “Ding” Darling (1876-1962) was a newspaper editorial cartoonist and duck hunter. Because of his proconservation cartoons, he had become one America’s most prominent conservationists by the early 1930s. Joseph P. Knapp (1864-1951) was a prominent businessman, philanthropist, conservationist, and duck hunter who, like Darling, had become concerned about the decline of waterfowl populations. Both worked to reverse this duck decline. Darling was appointed chief of the Bureau of Biological Survey in 1934 by President Franklin Roosevelt. During his short tenure as its chief (1934-1935), he focused the Bureau’s mission more on wildlife conservation and he oversaw the expansion of the national wildlife refuge system. In 1930, Knapp founded the More Game Birds in America Foundation. This Foundation through its waterfowl surveys documented that western Canada was the major breeding ground of ducks in North America. This resulted in the Foundation establishing Ducks Unlimited, Inc. in the US and Ducks Unlimited (Canada) in 1937. DU, Inc. would raise money, and DU (Canada) would spend this money in western Canada on wetland conservation and restoration projects. Both men helped to slow down the loss of wetlands by stressing the need for the public and private sectors to conserve and restore them as waterfowl habitat. They also shaped future wetland science by creating opportunities for the employment of wetland scientists.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.003
Scholarly communication0.0010.002
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 head0.011
GPT teacher head0.289
Teacher spread0.277 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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