The influence of forest site on rate and extent of soil compaction and profile disturbance of skid trails during ground-based harvesting
Bibliographic record
Abstract
Soil compaction has been considered a principal form of damage associated with logging, restricting root growth and reducing productivity. The rate and extent of soil compaction on skid trails was measured at six field locations covering a range of dry and wet forests. Data was collected for up to 21 passes of a laden logging machine. A similar extent of compaction, averaging 0.17 g·cm-3 increase in total soil bulk density (BD), was recorded for all field sites despite substantial site and soil differences. On average, 62% of the compaction in the top 10 cm of the soil occurred after only one pass of a laden logging machine. The environment under which soils had formed played a major role in determining the BD of the undisturbed soil. Compaction was strongly related to the original BD, forest type, and soil parent material. Soil strengths obtained in the field fell below levels found to restrict root growth. However, reduction in macropores, and the effect of that on aeration and drainage could reduce tree growth. On the wettest soils logged, machine forces displaced topsoils rather than causing compaction in situ. Recommended logging methods and implications for the development of sustainability indices are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".