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Record W1909306563 · doi:10.1002/wsb.397

A comparison of avian habitat in forest management plans produced under three different certification systems in Ontario, Canada

2014· article· en· W1909306563 on OpenAlexfundaboutno aff
David Euler

Bibliographic record

VenueWildlife Society Bulletin · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersMinistry of Natural Resources
KeywordsWoodpeckerCertificationForest managementHabitatCertified woodWildlifeGeographySustainable forest managementWildlife managementBiodiversityForest ecologyTaigaEcologyForestryAgroforestryEcosystemBiology

Abstract

fetched live from OpenAlex

ABSTRACT Considerable discussion and theoretical reviews of forest certification systems have been published that outline the potential impact of forest certification on biodiversity and wildlife habitat. Three common approaches to forest certification used in Canada include the Forest Stewardship Council, the Canadian Standards Association, and the Sustainable Forestry Initiative. In this study, I reviewed forest management plans in Ontario, Canada, and compared the plans' potential impact on habitat for 6 species of birds that use mature and older forests: pileated woodpecker ( Dryocopus pileatus ), ruby‐crowned kinglet ( Regulus calendula ), boreal chickadee ( Poecile hudsonicus ), black‐backed woodpecker ( Picoides arcticus ), great gray owl ( Strix nebulosa ), and red‐breasted nuthatch ( Sitta canadensis ). The purpose was to determine whether the different certification systems in use resulted in different impacts on avian habitat, and to compare certified forest management plans with plans prepared for un‐certified forest units. Based on 27 Forest Management Plans in Ontario, there seems to be little reason to believe that certified forests are more likely to protect and conserve habitat for these 6 bird species that use older forests than are forests that are not certified. © 2014 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.678

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 head0.018
GPT teacher head0.224
Teacher spread0.205 · 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

Citations20
Published2014
Admission routes2
Has abstractyes

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