Deforestation mapping sampling designs for Canadian landscapes
Bibliographic record
Abstract
Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canada’s forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard error / sample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%–25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%–40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".