Effects of logging debris treatments on five-year development of competing vegetation and planted Douglas-fir
Bibliographic record
Abstract
Although considerable research has focused on the influences of logging debris treatments on soil and forest regeneration responses, few studies have identified whether debris effects are mediated by associated changes in competing vegetation abundance. At sites near Matlock, Washington, and Molalla, Oregon, studies were initiated after timber harvest to quantify the effects of three logging debris treatments (dispersed, piled, or removed) on the development of competing vegetation and planted Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii ). Each debris treatment was replicated with initial and annual vegetation control treatments, resulting in high and low vegetation abundances, respectively. This experimental design enabled debris effects on regeneration to be separated into effects mediated by vegetation abundance and those independent of vegetation abundance. Two to three years after treatment, covers of Scotch broom ( Cytisus scoparius (L.) Link) at Matlock and trailing blackberry ( Rubus ursinus Cham. & Schltdl.) at Molalla were over 20% greater where debris was piled than where it was dispersed. Debris effects on vegetation abundance were associated with 30% reductions in the survival of Douglas-fir at Matlock (r2 = 0.62) and the stem diameter at Molalla (r2 = 0.39). Douglas-fir survival and growth did not differ among debris treatments when effects were evaluated independent of vegetation abundance (i.e., with annual vegetation control), suggesting negligible short-term effects of debris manipulation on soil productivity.
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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 | 0.001 | 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".