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Record W2147160327 · doi:10.5558/tfc85695-5

Ecological forestry at National Wildlife Refuges: Experiences from Seney National Wildlife Refuge and Kirtland’s Warbler Wildlife Management Area, USA

2009· article· en· W2147160327 on OpenAlexvenueno aff
R. Gregory Corace, P. Charles Goebel, David M. Hix, T. W. Casselman, Nancy E. Seefelt

Bibliographic record

VenueThe Forestry Chronicle · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServiceCentral Michigan University
KeywordsWildlifeGeographyWildlife refugeForest managementForest ecologyEcologyEndangered speciesEcosystemEnvironmental resource managementEcosystem managementWildlife managementBeechDisturbance (geology)ForestryHabitatEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Although land management over much of the history of the U.S. Fish and Wildlife Service’s National Wildlife Refuge System (NWRS) has emphasized single-species management, recent policy has encouraged land managers to focus on broader ecosystem restoration goals. One framework for forest ecosystem management that is becoming more popular in the NWRS and other federal and state resource agencies has been termed “ecological forestry”—an approach to forest ecosystem management where the focus is on incorporating an understanding of the outcomes of natural disturbances and stand development processes into designing silvicultural practices. This approach stresses understanding the effects of natural disturbances on biological legacies, structural and compositional heterogeneity, and the recovery periods between disturbance events (including how this recovery period influences stand complexity). Recently, resource managers and ecologists from Seney National Wildlife Refuge, The Ohio State University, and Central Michigan University have partnered to examine how these guiding principles can be integrated into NWRS forest ecosystem management. Specifically, we are partnering to develop management strategies to help: 1) restore the once extensive mixed-pine forest ecosystems of eastern Upper Michigan; 2) mitigate the effects of the beech-bark disease complex on American beech (Fagus grandifolia Ehrh.), a foundation species in northern hardwood forests of eastern North America; and 3) promote more natural forest patterns for wildlife species of young jack pine (Pinus banksiana Lamb.) forest ecosystems, including the federally endangered Kirtland’s warbler (Dendroica kirtlandii). These efforts are ongoing and will continue to be monitored over time. However, initial collaborations suggest that the NWRS provides an excellent crucible to study the application of ecological forestry principles and develop novel ways to manage forest ecosystems. Key words: ecological forestry, forest restoration, Kirtland’s warbler, National Wildlife Refuge, U.S. Fish and Wildlife Service

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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