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Record W2095193445 · doi:10.1139/x07-246

Joint production of timber, carbon, and wildlife habitat in the Canadian boreal plains

2008· article· en· W2095193445 on OpenAlexaffvenueabout
Geoffrey R. McCarney, Glen W. Armstrong, Wiktor Adamowicz

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of Alberta
Fundersnot available
KeywordsWildlifeHabitatTaigaEnvironmental scienceForest managementBiomass (ecology)Carbon sequestrationLoggingAgroforestryEcologyGeographyForestry

Abstract

fetched live from OpenAlex

This study investigates the relationships and trade-offs between forest carbon management, sustained timber yield, and the production of wildlife habitat to provide a more complete picture of the costs and challenges faced by forest managers for a particular case study in Canada’s boreal mixedwood region. The work presented is an extension of a previously published model that analysed the joint production of timber supply and wildlife habitat using a natural disturbance model approach to ecosystem management. The primary contribution of the present study is the detailed incorporation of a carbon budget model into the framework developed previously. Using the Carbon Budget Model of the Canadian Forest Sector, dynamics specific to separate biomass and dead organic matter carbon pools are represented for individual forest cover types. Results indicate the potential for cost thresholds in the joint production of timber supply and carbon sequestration. These thresholds are linked to switch points in the decision between multiple use and specialized land management practices. Cobenefits in the production of carbon and wildlife habitat are shown to depend on ecological parameters, harvest flow regulations, and incentives for timber supply provided by the market.

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)0.002
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 score0.071
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.049
GPT teacher head0.279
Teacher spread0.230 · 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

Citations30
Published2008
Admission routes3
Has abstractyes

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