Deforestation estimation for Canada under the Kyoto Protocol: A design study
Bibliographic record
Abstract
Deforestation is a persistently important issue locally, nationally, and internationally. It is of interest to the public, foresters, environmental organizations, and governments, yet it is difficult to obtain reliable estimates of its extent and nature. Climate change and the role of forests has given a large impetus for formalizing reporting on deforestation. Under the proposed Kyoto Protocol, industrialized nations are required to report on the carbon consequences of deforestation and include them in their greenhouse gas emissions accounting. Canada must develop measurement systems to report on the area of deforestation and the carbon stock loss. Possible data sources include the new plot-based National Forest Inventory (NFI), land use records, and satellite remote sensing. The NFI is a network of 2 × 2 km plots at a 20 km spacing for which land cover and stand attributes are interpreted from medium-scale aerial photography. In this study, medium-resolution satellite imagery, such as Landsat Thematic Mapper (TM), was explored as a potential tool for deforestation estimation and a survey of available land use records was conducted. Factors affecting the utility of each data source and various system design options were examined. An integrated system is suggested that utilizes the NFI as a base, augmented by satellite remote sensing plots and supported by local records as appropriate.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".