Embedding science and innovation in forest management: Recent experiences at Millar Western in west-central Alberta
Bibliographic record
Abstract
Research from scientists embedded within Millar Western’s forest management planning process over the last 14 years was enabled by strong corporate leadership, cooperation by Alberta’s Ministry of Sustainable Resource Development, and funding by the Forest Resource Improvement Association of Alberta. Results of the supporting research are described in the articles that follow and are important contributions to Canada’s commitment to sustainable forest management (SFM). The process is as noteworthy as the results and is the subject of this paper. When scientists and practitioners work closely together in developing a forest management plan, as they have in this case, there is a much greater opportunity for science-based emergent strategies to be created and applied through the personal interactions among scientists and practitioners. For example, input from the science-based collaborators influenced the harvest schedule in the detailed forest management plan to minimize negative effects on water flow, biodiversity and fire risk. This approach to SFM is one of many being developed in Alberta. The diversity of input has clear benefits, not the least of which is the maintenance of innovation and intellectual enterprise in support of SFM. Key words: forest management planning, forest science, innovation, Alberta, biodiversity, timber supply, guidelines
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".