Uncertainty assessment of a polygon database of soil organic carbon for greenhouse gas reporting in Canada’s Arctic and sub-arctic
Bibliographic record
Abstract
Canada’s Arctic and sub-arctic consist 46% of Canada’s landmass and contain 45% of the total soil organic carbon (SOC). Pronounced climate warming and increasing human disturbances could induce the release of this SOC to the atmosphere as greenhouse gases. Canada is committed to estimating and reporting the greenhouse gases emissions and removals induced by land use change in the Arctic and sub-arctic. To assess the uncertainty of the estimate, we compiled a site-measured SOC database for Canada’s north, and used it to compare with a polygon database, that will be used for estimating SOC for the UNFCCC reporting. In 10 polygons where 3 or more measured sites were well located in each polygon, the site-averaged SOC content agreed with the polygon data within ±33% for the top 30 cm and within ±50% for the top 1 m soil. If we directly compared the SOC of the 382 measured sites with the polygon mean SOC, there was poor agreement: The relative error was less than 50% at 40% of the sites, and less than 100% at 68% of the sites. The relative errors were more than 400% at 10% of the sites. These comparisons indicate that the polygon database is too coarse to represent the SOC conditions for individual sites. The difference is close to the uncertainty range for reporting. The spatial database could be improved by relating site and polygon SOC data with more easily observable surface features that can be identified and derived from remote sensing imagery.
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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".