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Record W2972142906 · doi:10.1002/wsb.1002

The history and importance of private lands for North American waterfowl conservation

2019· article· en· W2972142906 on OpenAlexaff
Michael G. Brasher, James J. Giocomo, David A. Azure, Anne M. Bartuszevige, Mark E. Flaspohler, Dean E. Harrigal, Brian W. Olson, John Pitre, Randy W. Renner, Scott E. Stephens, Josh L. Vest

Bibliographic record

VenueWildlife Society Bulletin · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsDucks Unlimited Canada
Fundersnot available
KeywordsWaterfowlWildlifeIncentiveEasementBusinessHabitatLand tenureWildlife conservationHabitat conservationResource (disambiguation)Environmental planningEnvironmental resource managementGeographyNatural resource economicsAgriculturePolitical scienceEcologyEconomics

Abstract

fetched live from OpenAlex

ABSTRACT Waterfowl conservation in North America provides an example of an abundant wildlife resource that was driven to alarmingly low levels as a result of unregulated exploitation of its populations and habitats, but which has since recovered because of cooperative efforts across multiple countries. Waterfowl conservation in North America began in earnest during the late 19th and early 20th centuries and has developed through international treaties and national policy as well as regional partnerships and supporting efforts of private landowners. In recent years, significant accomplishments have been realized through public–private partnerships that use local knowledge and engagement with stakeholders to develop conservation programs that are compatible with landowner interests and existing farming or ranching operations. Through the presentation of case studies from across North America, we demonstrate there exists no single ‘best’ program or framework for conserving waterfowl habitat on private lands, although a common denominator for success is robust support from an energized and resourceful partnership. Financial incentives provided a positive encouragement for participation, but private land programs will be most effective long term when they explicitly incorporate the needs of private landowners and generate benefits beyond provision of wildlife habitat. Successful conservation of waterfowl populations into the future will require a suite of programs and strategies and will hinge on our ability to develop conservation solutions that provide mutual benefits to waterfowl and an increasingly diverse private‐landowner base. © 2019 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.257

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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