Northern White-Cedar Regeneration Dynamics on the Penobscot Experimental Forest in Maine: 40-Year Results
Bibliographic record
Abstract
Abstract The objective of this study was to assess the long-term dynamics of northern white-cedar (Thuja occidentalis L.) seedling and sapling growth and mortality on the Penobscot Experimental Forest in Maine. Data collected between 1965 and 2005 in four twice-replicated partial cutting treatments were analyzed. White-cedar seedlings established in all treatments despite relatively high white tailed-deer (Odocoileus virginianus Zimmerman) population densities. However, although it appears that regeneration cohorts of associated softwoods increased in size over time, the white-cedar cohort did not. Ingrowth of white-cedar from the seedling to sapling stage was lower than the combined rates of sapling mortality and recruitment to the pole stage; sapling density of this species in 2005 was >80% less than it was at the start of the measurement period. Sapling mortality was high, and recruitment to larger size classes was low, although mortality decreased and recruitment increased as sapling size increased. Browsing was prolific; 90% of white-cedar seedlings and small saplings showed signs of browse in 2005. Overall, white-cedar sapling growth was slow, with an estimated 100 years needed to grow from small sapling to merchantable size in the study stands. Efforts to release white-cedar saplings through precommercial treatment and control of browsing pressure are recommended.
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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 | 0.001 | 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".