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Record W2025539441 · doi:10.1139/x08-046

Future carbon storage in harvested wood products from Ontario’s Crown forests

2008· article· en· W2025539441 on OpenAlexafffundvenueabout
Jiaxin Chen, S. J. Colombo, Michael T. Ter‐Mikaelian, Linda S. Heath

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsOntario Forest Research Institute
FundersGovernment of Ontario
KeywordsEnvironmental scienceForestryCarbon stockFossil fuelCarbon sinkCrown (dentistry)Greenhouse gasClimate changeGeographyWaste managementEngineeringEcology

Abstract

fetched live from OpenAlex

This analysis quantifies projected carbon (C) storage in harvested wood products (HWP) from Ontario’s Crown forests. The large-scale forest C budget model, FORCARB-ON, was applied to estimate HWP C stock changes using the production approach defined by the Intergovernmental Panel on Climate Change. Harvested wood volume was converted to C mass and allocated to four HWP end-use categories: in use, landfill, energy, and emission. The redistribution of C over time among HWP end-use categories was calculated using a product age-based C-distribution matrix. Carbon emissions for harvest, transport, and manufacturing, as well as emission reductions from the use of wood in place of other construction materials and fossil fuels were not accounted for. Considering the wood harvested from Ontario Crown forests from 1951 to 2000 and the projected harvest from 2001 to 2100, C storage in HWP in use and in landfills is projected to increase by 3.6 Mt·year –1 during 2001–2100, with an additional 1.2 Mt·year –1 burned for energy. Annual additions of C projected for HWP far outweighs the annual increase of C storage in Ontario’s Crown forests managed for harvest, which is projected to increase by 0.1 Mt·year –1 during the same period. These projections indicate that regulated harvest in Ontario results in a steadily increasing C sink in HWP and forests. Uncertainties in HWP C estimation are also discussed.

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)0.001
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 score0.236
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.264
Teacher spread0.235 · 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

Citations47
Published2008
Admission routes4
Has abstractyes

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