Research Programs at the University of British Columbia Research Forests
Bibliographic record
Abstract
The Faculty of Forestry at the University of British Columbia manages two Research Forests and cooperates on a third Research Forest with the University of Northern British Columbia.These three properties have a total area of 24,705 hectares (ha) in four different climatic regions, ranging from coastal temperate rainforest to arid continental forest/steppe.The primary goal of the Research Forests is to support the education of forestry students.Active research and demonstration programs help to accomplish that goal.Two different approaches to research are used: Develop conditions suitable to hosting research projects in a variety of disciplines.The management of the Research Forests, including harvesting and silviculture programs, is used to develop conditions suitable for research and demonstration.Such projects are conducted by faculty members, students, and researchers outside the faculty and the university.Design and implement research projects to develop information that is important to the management of the forest estate.Research Forest staff are investigators in a number of active research projects.Several research projects are described to exemplify these two different approaches.One longstanding challenge is to ensure the availability of results and integrity of records to provide information useful in future research and extension projects.Research projects rigorously designed on large sites provide the best opportunity for future research and demonstration projects.Managing forestry operations while maintaining• research opportunities can be expensive, and requires willing cooperation amongst the staff.Demonstrating innovative management and maintaining a long-term perspective are our principle methods of supporting researchers.This allows us to establish rigorous studies on sites that will be useful in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.186 | 0.027 |
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".