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Record W2143508902 · doi:10.5558/tfc78397-3

The retention system:reconciling variable retention with the principles of silvicultural systems

2002· article· en· W2143508902 on OpenAlexaffvenue
Stephen J. Mitchell, William J. Beese

Bibliographic record

VenueThe Forestry Chronicle · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsVancouver Island UniversityUniversity of British Columbia
Fundersnot available
KeywordsStock (firearms)ProductivitySilvicultureEcosystemEnvironmental resource managementForest managementVariable (mathematics)Ecosystem servicesLoggingBusinessEnvironmental scienceYield (engineering)Ecosystem managementOperations managementAgroforestryForestryEconomicsEcologyGeographyMathematics

Abstract

fetched live from OpenAlex

The philosophy of ecosystem management seeks a balance between protecting natural systems and using them to meet societal demands. The objectives of silvicultural systems listed in standard texts focus on the sustained production of timber and maintenance of quality growing stock. These objectives need updating for situations where the broader goal is to sustain ecosystem function and productivity. The "retention system" recently adopted in British Columbia is a silvicultural system designed to implement the "variable retention" (VR) approach to harvesting. With VR, trees are retained to meet ecological objectives such as maintaining structural heterogeneity and protecting biological legacies. The contribution of retained trees to yield or regeneration may be low or even negative. Among the challenges in implementing the retention system is the adjustment of yield expectations and target stand projections to account for the expected health and vigour of the future stand. Key words: silvicultural system, retention system, variable retention, ecosystem management

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.034
GPT teacher head0.182
Teacher spread0.148 · 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 source (direct Gemma or distilled Codex), 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

Citations154
Published2002
Admission routes2
Has abstractyes

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