Six years of plant community development after clearcut harvesting in western Washington
Bibliographic record
Abstract
What roles do ruderals and residuals play in early forest succession and how does repeated disturbance affect them? We examined this question by monitoring plant cover and composition on a productive site for 6 years after clearcutting and planting Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco). The replicated experiment included three treatments: vegetation control with five annual herbicide applications superimposed over two levels of slash removal (bole only or total tree plus most other wood) and an untreated control. Three species groups were analyzed: native forest, native ruderals, and exotic ruderals. Without vegetation control, the understory was rapidly invaded by exotic ruderals but was codominated by native and exotic ruderals by year 6. Douglas-fir cover surpassed covers in the three species group covers at least 3 years sooner with herbicide treatments than without. Species richness and coverage were lower for all species groups with vegetation control than without vegetation control. The effects of organic matter removal were much less than that of vegetation control. As predicted by the Intermediate Disturbance Hypothesis, repeated vegetation control resulted in declining cover and richness; however, native forest species were surprisingly resilient, maintaining as much or more cover and richness as the ruderal groups.
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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 | 0.001 | 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".