Demand for nonprovisioning ecosystem services as a driver of change in the Canadian boreal zone<sup>1</sup>
Bibliographic record
Abstract
The Canadian boreal zone provides ecosystem services from local to global scales. Either directly or indirectly, demands for these services have and will continue to serve as drivers of change in the region. Here we present evidence for past, present, and potential future demand for maintaining nonprovisioning ecosystem services (NPrES), defined as indirect and nonmarketable services obtained from ecosystems as a driver of change in the boreal zone. Our evidence of demand stems from federal and provincial policies, actions by Indigenous peoples, and nongovernmental initiatives that aim to maintain the sustainability of natural resource extraction and ecosystem condition of the boreal. Presently, the demand for NPrES influences decisions related to natural resource development (e.g., forestry) that in turn impacts the condition of the boreal zone. Informed by the present conditions and past trends, three future scenarios to the year 2050 are presented that contrast in their trajectory—status quo, increased demand for NPrES, and decreased demand for NPrES. We also summarize the interactions among other drivers of change in the boreal and the synergies and trade-offs among the different types of demand for NPrES. Ultimately, sustainability of the boreal zone and the ecosystem services it provides will result from a complex suite of interacting drivers of change, where the balance of demands for provisioning and NPrES will continue to influence regional conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".