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The Need for Coherence Between Waterfowl Harvest and Habitat Management

2006· article· en· W2039268779 on OpenAlexaff
Michael C. Runge, Fred A. Johnson, Michael G. Anderson, Mark D. Koneff, Eric T. Reed, SETH E. MOTT

Bibliographic record

VenueWildlife Society Bulletin · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change CanadaDucks Unlimited Canada
Fundersnot available
KeywordsWaterfowlScope (computer science)Plan (archaeology)HabitatPopulationManagement by objectivesAdaptive managementEnvironmental resource managementConservation PlanBusinessEnvironmental planningWork (physics)Habitat conservationGeographyEcologyEngineeringComputer scienceBiologyEnvironmental scienceMarketing

Abstract

fetched live from OpenAlex

Two of the most significant management efforts affecting waterfowl populations in North America are the North American Waterfowl Management Plan (the Plan) and Federal harvest management programs. Both the Plan and harvest management are continental in scope, involve an extensive group of stakeholders, and rely on adaptive processes of biological planning, implementation, and evaluation. The development of these programs has occurred independently, however, and there has been little explicit recognition that both harvest and habitat effects should be considered for coherent management planning and evaluation. For example, the harvest strategy can affect whether population objectives of the Plan are met, irrespective of the success of the Plan's habitat conservation efforts. Conversely, habitat conservation activities under the Plan can influence harvest potential and, therefore, the amount of hunting opportunity provided. It seems increasingly clear that the Plan's waterfowl population objectives can only be useful for conservation planning and evaluation if they are accompanied by an explicit specification of the harvest strategy and environmental conditions under which they are to be achieved. This clarification also is necessary to ensure that Plan population objectives are not attained solely through the reduction of hunting opportunity. We believe then that it is imperative that these key waterfowl-management programs work to harmonize their objectives. Harvest management programs and the Plan ought to be working toward the same ends, but that is not possible so long as the mutually reinforcing relationship of these programs is obscured by ambiguities in their management objectives.

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 score0.027
Threshold uncertainty score0.507

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 studies0.0010.001
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations43
Published2006
Admission routes1
Has abstractyes

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