The real wealth of the Mackenzie region : assessing the natural capital values of a northern Boreal ecosystem
Bibliographic record
Abstract
Boreal ecosystems store more carbon in peatlands than any other land-based ecosystem. The carbon values in the Mackenzie watershed add up to 56 per cent of the total estimated non-market value of all ecosystem services in the watershed. This study demonstrated that the natural capital value of the Mackenzie region makes a significant contribution to the social, cultural, and economic health of Canadians. The study provided estimates and methods by which natural capital accounts can be developed on regional scales and measure changes in ecosystem values. The report provided comprehensive inventories of natural capital values and emphasized that research must be conducted to assess the relationship between industrial development and natural capital. Active monitoring of the pace, scale and extent of anthropogenic changes in the landscape must be conducted on a regular basis in order to safeguard natural capital values related to water quantity and quality, carbon storage and sequestration in Canada's boreal region. It was concluded that regulatory and voluntary carbon trading regimes can ensure that climate related costs are integrated into market decisions. 6 tabs., 10 figs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".