Growing our future: Assessing the outcome of afforestation programs in Ontario, Canada
Bibliographic record
Abstract
The United Nations Framework Convention on Climate Change (UNFCCC) requires its signatories, including Canada, to estimate and report their annual greenhouse gas (GHG) emissions and removals. Forests are an important natural resource as they slow the accumulation of atmospheric carbon through the process of carbon sequestration. Due to the role of forests as carbon sinks, governments consider afforestation projects as feasible climate change mitigation strategies. This article outlines a spatially-explicit approach to validating afforestation data in Ontario, Canada. Validation is a user-supervised process that uses satellite imagery, remote sensing tools, and other auxiliary data to confirm the presence of seedlings planted through Forests Ontario’s 50 Million Tree program. Of the 12 466 hectares assessed, 83% is identified as afforested, 6% is not afforested and 10% is not determined. The area classified as successful afforestation is used as input for the Generic Carbon Budget Model (GCBM), to simulate afforestation effects on carbon stocks. Our findings show the afforestation activities will create a small carbon sink by 2060. From this project, it is evident that spatial validation of afforestation data is feasible, although the collection of additional standardized auxiliary data is recommended for future afforestation projects, if carbon benefits are to be reported.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".