Soil disturbance and 10-year growth response of coast Douglas-fir on nontilled and tilled skid trails in the Oregon Cascades
Bibliographic record
Abstract
We (i) quantified effects of skidder yarding on soil properties and seedling growth in a portion of western Oregon, (ii) determined if tilling skid trails improved tree growth, and (iii) compared results with those from an earlier investigation in coastal Washington. Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) seedlings were hand planted at eight recent clearcuts in skid ruts in either nontilled or tilled trails, in adjacent soil berms, and in adjacent logged-only portions. Four and 5 years after skidding, rut depths averaged 15 cm below the original soil surface; mean fine-soil bulk density (030 cm depth) below ruts of nontilled trails exceeded that on logged-only portions by 14%. Height growth on nontilled trails averaged 24% less than on logged-only portions in year 4 after planting and decreased to 6% less in year 7. For years 810, mean height growth was similar for all treatments. Reduced height growth lasted for about 7 years compared with 2 years for coastal Washington. Ten years after planting, trees in skid-trail ruts averaged 10% shorter with 29% less volume than those on logged-only portions. Tillage improved height and volume growth to equal that on logged-only portions. Generalizations about negative effects of skid trails on tree growth have limited geographic scope.
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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 | 0.000 |
| 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".