Overstory and understory development in thinned and underplanted Oregon Coast Range Douglas-fir stands
Bibliographic record
Abstract
Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests managed for timber in western Oregon frequently lack structure and diversity associated with old-growth forests. We examined thinning effects on overstory and understory development for 8 years after treatment. Three 30- to 33-year-old Oregon Coast Range plantations were partitioned into four overstory treatments: unthinned (~550 trees/ha) and lightly (~250 trees/ha), moderately (~150 trees/ha), and heavily (~75 trees/ha) thinned. Within each overstory treatment, two understory treatments were established: underplanted with Douglas-fir and western hemlock (Tsuga heterophylla (Raf.) Sarg.) or not underplanted. Thinning increased overstory stem growth, crown expansion, and retained crown length. Thinned overstory canopies began to close rapidly the third year after thinning, decreasing % skylight by approximately 2%/year, whereas % skylight in unthinned stands increased slightly. All seedlings planted in unthinned stands died, whereas eighth year survival in thinned stands averaged 88%. Natural regeneration densities and distributions were highly variable. Understory shrub cover was reduced by harvesting disturbance but recovered by the fifth year. Thinning increased understory plant species diversity, and no shrub species were lost. Thinning to low densities and underplanting has the potential to accelerate development of multilayered stands characteristic of old-growth Douglas-fir forests.
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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".