Improving carbon storage and greenhouse gas emissions avoidance through harvested wood products use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Afforestation can mitigate climate change by creating new carbon sinks and increasing wood supply. However, climate change can impact the growth of trees in afforested areas and affect their characteristics, and the harvested wood products that can be manufactured from them. This study aimed to quantify to what extent the quality of the wood supply directed to primary processing is influenced by climate change and alters the carbon storage of wood products. A multi-model approach was used to estimate the carbon stocks in harvested biomass resulting from plantations of black spruce on open woodlands and hybrid poplar on abandoned farmlands in Québec (Canada) under a gradient of climate forcing projections. Results suggest that increased climate forcing negatively impacts the quality of the harvested wood product basket and influences the relative amount of lumber vs. pulpwood. However, according to our assumptions, the decay of solid wood products in landfills produced more methane emissions than paper, which may constrain their climate change mitigation potential in the absence of methane capture or flaring. The cascading use of solid wood products in bioenergy at the end of their service life significantly reduced overall emissions. This study highlights how comprehensive afforestation strategies can, in the long term, be used to maximize the carbon storage potential of harvested wood products sourced from new plantations, as long as these strategies also include better use of pulp-quality wood, improved cascading use at the end-of-life of wood products and, most importantly, the avoidance of methane emissions from landfilled wood.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it