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Record W4390453717 · doi:10.1672/ucrt083-50

Men of the Marshes: Paul L. Errington and H. Albert Hochbaum

2023· article· en· W4390453717 on OpenAlexaboutno aff
Arnold van der Valk

Bibliographic record

VenueWetland Science and Practice · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsWaterfowlWildlifeWetlandMarshHabitatGeographyPopulationPredationEcologyWildlife conservationDeltaSociologyDemographyEngineeringBiology

Abstract

fetched live from OpenAlex

Paul L. Errington (1902-1962) and H. Albert Hochbaum (1911-1988) were pioneering wildlife biologists whose research focused on muskrats and waterfowl, respectively. Their publications, especially their books, stressed the importance of wetlands as wildlife habitats. Errington spent his entire professional career at Iowa State University. Much of it studying muskrat population dynamics in prairie potholes. His work on the predation of muskrats and other species changed how predators were perceived from negative to positive for ecologists, hunters, and the general public. Hochbaum spent his entire professional career as the scientific director of the Delta Water Research Station in Canada. Because of his influential publications and those of the many graduate students at Delta whose research he watched over, Hochbaum built Delta into one of the premier waterfowl research institutions in the world. Errington’s and Hochbaum’s books influenced ecologists and the general public, especially those interested in wildlife conservation. They played a significant role in the development of wetland science by demonstrating the importance of wetlands as wildlife habitats and highlighting the urgent need for wetland conservation. Their advocacy contributed to the gradual shift in North American attitudes toward wetlands from negative to positive.

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)0.001
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 score0.051
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.016
GPT teacher head0.242
Teacher spread0.227 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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