Industrial innovation and infrastructure as drivers of change in the Canadian boreal zone<sup>1</sup>
Bibliographic record
Abstract
Much of Canada’s industrial sector is driven by natural resources and relies heavily on provisioning services supplied by the boreal zone. However, the sometimes intensive processes used by resource-based industries and their associated infrastructure have significantly altered the region, creating concerns over the future socio-ecological health of the boreal zone. Addressing these concerns will require industries reliant on natural resources from the boreal zone to innovate their processes, management, and infrastructure to improve extraction efficiency while contributing to society’s increasing expectations related to sustainability. Here, we explore past, current, and future trends in industrial innovation and infrastructure in the boreal zone for forestry, mining, pulp and paper, oil and gas, and renewable sources of power generation. We assess the role of innovation on the future socio-ecological state of the boreal zone by considering interactions between innovation in industry and infrastructure and other key drivers of change in the boreal, such as atmospheric changes, changing demands for nonprovisioning and provisioning ecosystem services, governance, and demographics and social values. We present future scenarios highlighting three divergent trajectories of change in boreal ecosystems based on past and current states of innovation in industry and infrastructure. We suggest that minimizing impacts of natural resource extraction activities in the boreal zone will only be possible through innovation directly focused on reducing the human footprint on the landscape. Innovation in the information technology sector related to process, management, and end products within these industries and placing greater emphasis on cross-sectoral collaboration will be key to achieving this goal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".