Determination of above ground carbon in Canada's forests-a multi-source approach
Bibliographic record
Abstract
Canada is a signatory to the Kyoto Protocol and must report on reforestation, afforestation and deforestation activities since 1990. Reporting commitments also include a baseline estimate of forest carbon stocks in 1990 and the monitoring of changes in carbon stocks leading up to the reporting period 2008 to 2012. Canada has 10% of the world's forests (418 million hectares), which account for a significant amount of stored carbon. The determination of above-ground carbon stocks in the forest can be based on several sources: remote sensing, models of vegetation growth, book-keeping carbon models, and traditional forest inventories. Estimating above-ground carbon with remote sensing requires the fusion and integration of remote sensing data with topographic, forest cover and other geospatial information. Multi-temporal LANDSAT TM imagery was used in conjunction with GIS data to compute above-ground biomass from which the carbon content is determined. In addition to biomass, other key factors, which play a role in the determination of carbon stocks, include species and age distribution, forest structure, and climate variables. The paper reports on remote sensing experiments to determine the above-ground carbon stocks for a test site near Hinton, Alberta, Canada. It is expected that this approach will be useful in supporting Canada's reporting commitments on the sustainability its forest resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".