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Record W1579254597 · doi:10.22230/jem.2006v7n2a546

An assessment of critical assumptions supporting the timber supply modelling for mountain-pine-beetle-induced allowable annual cut uplift in thePrince George Timber Supply Area

2006· article· en· W1579254597 on OpenAlexaff
John Pousette, Chris Hawkins

Bibliographic record

VenueJournal of Ecosystems and Management · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMountain pine beetleWildlifeEnvironmental scienceEnvironmental resource managementForestryEcologyGeography

Abstract

fetched live from OpenAlex

To address the mountain pine beetle epidemic, the allowable annual cut (AAC) for the Prince George Timber Supply Area (TSA) has been increased by 5.7 million m3 to 14.944 million m3. Timber supply model forecasts supporting AAC uplift decisions show a significant mid-term timber supply falldown. Timber supply modelling undertaken for the Prince George TSA in support of the recent AAC decision has not incorporated mortality in stands less than 60 years old, has generally considered the shelf life of beetlekilled wood to be 5 years for use as dimension lumber and 5 additional years for use as products of reduced fibre quality, and has assumed that pine timber makes up at least 78% of the total harvest in the short term. Current research suggests that certain of these assumptions may be optimistic. Refinements to these important timber supply analysis assumptions may result in a mid-term timber supply falldown that is deeper and longer than originally forecast. If the current outbreak continues unabated, stands in which lodgepole pine represents over 70% of the volume will provide enough mature growth to satisfy the new AAC for a further 14 years. It may be that the current increased AAC will not be realized for this period of time because of economic and environmental reasons. Greater value may be gained by not harvesting pine-dominated stands that contain significant stand structure or advance regeneration. Retention of these stands could mitigate the negative effects to the environment, hydrology, and wildlife while increasing the volume available in the latter part of the mid-term. Barriers to forest licensees focussing harvest in these stands include current mill requirements, existing traditional operating areas, previously approved cutting permits, stumpage appraisal, accessibility, and other economic considerations.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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 designSimulation or modeling
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

Citations12
Published2006
Admission routes1
Has abstractyes

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