Assessment of harvest blocks generated from operational polygons and forest-cover polygons in tactical and strategic planning
Bibliographic record
Abstract
Manually designing harvest units for strategic planning is expensive. This paper compares blocking methods based on forest-cover polygons and manually designed harvest units. Routines are used to split and aggregate polygons into three block size distributions: (i) uniform 40-ha blocks; (ii) uniform 120-ha blocks; and (iii) by area, one-third 20 ha, one-third 60 ha, and one-third 150 ha. Three harvest rules that influence adjacency and the cutting of polygons within a block are applied to each block size distribution to compare forecasts generated by forest-cover and operational blocks. Generally, volume flows from the two methods deviate by less than 5%, and the highest deviations usually occur during the first 20 years. Projected landscape structure, as measured by interior forest area, is also similar under the two blocking methods. The results indicate that forest-cover data provide a reasonable alternative to manual blocking in tactical and strategic plans. This is significant because it removes an important barrier to timely and cost-effective planning, especially for large geographic problems where manual blocking is not an option.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".