Productivity and Cost of Partial Harvesting Method to Control Mountain Pine Beetle Infestations in British Columbia
Bibliographic record
Abstract
Abstract Small patch cutting (<1 ha in size) in mature lodgepole pine (Pinus contorta Douglas var. latifolia Engelmann) stands has been introduced in central British Columbia, Canada to slow the spread of mountain pine beetle (Dendroctonus ponderosae Hopk.) populations. This practice is locally referred to as “Snip and Skid” logging. This article addresses the operational challenges of implementing the method, with an emphasis on the cost of each phase of logging. Total stump-to-truck expenses incurred with Snip and Skid logging in each patch at an average of C$17.00/m3 (C$14.98 to C$19.71/m3). However, if one includes other cost allowances, such as overhead and profit for the logging contractor, the overall cost is C$22.28/m3. These costs greatly increase when trees are smaller. Other costs for implementing the Snip and Skid method, such as planning and layout, ground probing, and baiting, further increase the total cost of implementation. Walking and low-bedding, that are not required for typical timber-production logging operations, accounted for 57% of the total delay in Snip and Skid logging. In this particular study, five trees were damaged per 100 m along the skid trails created to access the patches, but we found no high stumps or significant impacts on soils. West. J. Appl. For. 20(2):128–133.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".