How is forest management influencing carbon storage in sub-boreal forests:
Bibliographic record
Abstract
"A major question for Kyoto signatory nations such as Canada is what role our forests might play in meeting our greenhouse gas emission reduction commitments. The most important greenhouse gas affected by human activity is carbon dioxide (CO₂). Forests contain high amounts of carbon, which reflect the net balance between photosynthetic fixation of CO₂ into tree products - primarily cellulose - and the return of this carbon back to the atmosphere through respiration and fire. In this paper I integrate two aspects of my sub-boreal forest carbon research program, both conducted at the Aleza Lake Research Forest (ALRF) 60 km east of Prince George, BC. Clearcuts are sources of carbon for two reasons. First, primary forests contain >250 tonnes per hectare while new clearcuts contain <150 tonnes per hectare; loss of tree carbon is immediate and large. However, even after accounting for harvesting, clearcuts are net carbon sources (input CO₂ into the atmosphere) for >8 years after harvesting while belowground respiration exceeds photosynthesis. These carbon losses total 33 tonnes per hectare over 8 years, despite gains of 1 to 1.2 tonnes per hectare per year by the regrowing forest. Partial cut harvesting conserves the greatest amount of carbon. In balance, and after taking into account different forest management types, the entire upland region of the ALRF (6,035 hectares) has been essentially neutral with respect to carbon over the past 10 years. Thus, current forest practices and harvest levels at the ALRF appear to be sustainable with respect to carbon."
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".