Bibliographic record
Abstract
Land allocation use specialization is a forest management strategy designed to accommodate the wide array of values present on the forested landscape, having the potential to enhance both environmental and industrial uses of the forest. The details of such a strategy, however, are not fixed, being case specific. The challenge is to define the basis for zoning considering the multiplicity of values expected from each particular forestland base. Therefore, this work explores the implications of different zoning approaches for land allocation in the Prince George Forest District of central British Columbia, Canada. To do so, three objectives were set: a) evaluate the consequences of different zoning strategies on a specific forestland base b) examine the effects of different area proportions among categories on the land use allocation and c) explore how expected future climate change may affect land use allocation in the study area. The methodology consisted of defining the basic values expected by stakeholders from the forest land base and combining them using three different zoning approaches: three-zone, four-zone and multiple-zone. Results show that the zoning approach has major influences on the results, increasing spatial distribution and fragmentation with an increase in the number of zones. Furthermore, the increase in target area of a specific category results in its greater distribution over the landscape and better representation of the variety of landscapes found in the study area. Finally, climate change predictions can be proactively incorporated in land use plans, creating more robust land use plants. The methodology employed in this work enables the amalgamation of multiple sources of information to define forest values, it is flexible and it also provides spatially explicit allocation maps easy to assimilate. --P. ii.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".