POSSIBLE SYSTEMS FOR MEASURING AND REPORTING ON DEFORESTATION IN CANADA UNDER THE KYOTO PROTOCOL
Bibliographic record
Abstract
Abstract. A national system for determining and reporting on areas of deforestation is needed to fulfill Canada’s Kyoto Protocol reporting commitments. An enhanced National Forest Inventory (NFI) forms a reasonable national framework on which to build a deforestation reporting system. The NFI consists, at its core, of a grid system of 2x2 km plots on a 20 km spacing. The base design calls for forest parameters to be determined from aerial photo interpretation. A subset of plots are sampled on the ground. This core can be enhanced with data from other sources. One possible enhancement is the integration of the NFI plot system, medium resolution satellite remotely sensed data (e.g., Landsat TM), and existing land use records to improve measurements of deforestation in the context of the Kyoto Protocol. Important in such a system are what data are available and how to integrate the data. Key issues related to the appropriateness of public land use records are: what records are available; from who; their content, coverage and reliability; are they spatially explicit; are they yearly; are they legislated, regulated or voluntary; and are there access restrictions. Questions related to the potential use of satellite remote sensing include: what types of
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".