A procedure for economic analysis in forest resource planning at the operational level : a case study on the Seymour River resource folio of the Kamloops forest district
Bibliographic record
Abstract
Economic analysis of forest management alternatives could play an important and useful role in forest resource planning in British Columbia. The need for economic evaluation is emphasised by the increasing number of resource conflicts resulting from the dominance of the timber industry in the economy, the growing importance of other forest resource uses and the increasing awareness of the consequences of disturbing the forest environment. This thesis has developed a procedure for incorporating economic analysis into the operational level of forest resource planning. An examination of historical and current planning procedures showed a lack of economic input. Direct application of the economic theory of resource allocation was found to be impossible because of market imperfections, the absence of markets values for many forest resource values, and because of inadequate knowledge of the forest environment. The availability of timber monetary values and shadow-pricing techniques was relied upon for formulation of the procedure. Improved accounting systems for timber costs and increased experience with, and sophistication of, shadow-pricing methods, should be stressed to achieve short-term gains in the usefulness of the procedure and the "quality" of the results. To meet training, budget and information requirements, recommendations include emphasis on areas of severe resource conflicts and where data is available for thorough analysis. The need to consider the social impact of forest planning as well as economic efficiency effects was stressed. Several criteria of social impact were provided. A major part of the thesis was a comprehensive application of the procedure in a case study of the Seymour River resource folio in the Kamloops Forest District. This example showed the difficulty of obtaining definitive solutions to forest resource conflicts. However, with economic analysis an increased awareness of other possible solutions to the management problem was obtained, the relative importance of the different resource values became apparent, sensitive variables and data deficiencies were identified, and management alternatives providing increased overall benefits became evident. It is recommended that readers whose prime interest is in the practical aspects of this thesis should concentrate on Chapters VI and VII; the description of the procedure for analysis and the example. The earlier chapters provide the historical and theoretical background.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".