Soil Compaction Caused by Cut-to-Length Forest Operations and Possible Short-Term Natural Rehabilitation of Soil Density
Bibliographic record
Abstract
Our research explored the impact of forest machinery on soil when trafficking off-road through forest stands. In particular, we assessed soil compaction caused by harvesting operations. This study had two objectives: (i) Quantify the increase of soil bulk density (absolute and relative density) by forest machinery; and (ii) Analyze the persistence of soil compaction over a 5-yr period. Our research was innovative in three respects; 1. We assessed in-place soil density at exactly the same locations pre- and posttreatment with a nuclear moisture and density gauge. In this context, we consider treatment as forest machinery (harvester and forwarder) trafficking on forest soil. 2. After the treatment, we monitored soil density at identical locations through yearly assessments for 5 yr to identify possible natural rehabilitation patterns. 3. We related the measured field bulk densities to site specific maximum bulk densities derived by standard Proctor tests (concept of relative bulk density) to get a better understanding of the severity of off-road traffic impact on soil density changes. Our key findings on two research sites were: 1. On average, dry soil bulk density increased by 19% in machine tracks. 2. Machine impact was not just limited to vehicle tracks; we noticed an increase of soil bulk density >10% in 14 of 65 (21.5%) locations extending up to 1 m away from tracks. 3. Due to machine impact, field bulk density increases exceeded the 80% maximum bulk density threshold at 32% of all track locations, mostly in soil depths of 20 to 30 cm. 4. Monitoring soil density for 5 yr after the treatment indicated no natural rehabilitation (decrease) of soil density down to pretreatment levels.
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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 | 0.000 | 0.001 |
| 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".