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Record W2082770896 · doi:10.5558/tfc81256-2

Human perceptions of forest fragmentation: Implications for natural disturbance management

2005· article· en· W2082770896 on OpenAlexafffundvenue
Michael J. Meitner, Ryan Gandy, Robert G. D’Eon

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsPacific Insight Electronics (Canada)University of British Columbia
FundersCanadian Forest ServiceSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaU.S. Forest Service
KeywordsPerceptionFragmentation (computing)Status quoPreferenceForest managementEnvironmental resource managementDisturbance (geology)AgroforestryGeographyBusinessEnvironmental sciencePsychologyEcologyBiologyMathematicsPolitical science

Abstract

fetched live from OpenAlex

To test public perception and preference of forest fragmentation trends under current forest management practices, we solicited preferences for harvest patterns from 63 study participants before and after they were provided with educational material on the subject. In addition, we solicited preferences for harvest systems employing different retention patterns. Participants preferred harvest patterns tending away from small, dispersed harvest blocks (i.e., more fragmented) towards larger, more aggregated harvest blocks (i.e., less fragmented). This preference was more pronounced when participants were provided with information that stressed a less fragmented pattern as being ecologically beneficial. This result suggests that the public is willing to accept larger, more aggregated harvest blocks relative to the status quo, especially if provided with information that stresses benefits of that approach. However, participants clearly preferred a harvest system employing dispersed individual tree retention over other systems employing a more concentrated retention pattern. The combination of these results suggests that public acceptability of larger aggregated harvest blocks may depend on the amount of post-harvest retention involved, and that harvest systems employing dispersed individual tree retention will be preferred by the public. Key words: effects of information, environmental perception, forest fragmentation, forest management, human perception, natural disturbance

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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 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

Citations15
Published2005
Admission routes3
Has abstractyes

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