Soil compaction from cut-to-length thinning operations in young redwood forests in northern California
Bibliographic record
Abstract
In northern California, United States, a cut-to-length (CTL) system was recently used for the first time to harvest young redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) forests. Landowners and public agencies in this region have been concerned about the potential negative impacts of CTL on soils during wet-season harvest operations. To determine soil impacts, we measured changes in soil bulk density (BD) and hydraulic conductivity (HC) after CTL operations in May and August. Soil samples were collected at two locations (track and center) along forwarder trails and at a reference point at three soil depths (0–5, 10–15, and 20–25 cm), and HC samples were collected only at the 0–5 cm soil depth from the same sample points. We found a significant difference in BD between the reference point and track at 0–5 cm, which decreased as soil depth increased. There was a negative correlation between initial BD values and percent increase of BD, supporting the fact that the percent increase in BD was high at the soil surface (25%–30%), but BD did not exceed 1.13 Mg·m –3 at the 0–5 cm depth. However, our HC results were different from what we expected and were not as consistent as the BD results, as the HC data had much higher variability.
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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 | 0.000 | 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".