Multi-decadal establishment for single-cohort Douglas-fir forests
Bibliographic record
Abstract
The rate at which trees regenerate following stand-replacing wildfire is an important but poorly understood process in the multi-century development of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and western hemlock (Tsuga heterophylla (Raf.) Sarg.) forests. Temporal patterns of Douglas-fir establishment reconstructed from old-growth forests (>450 year) have generated contradictory models of either rapid (<25 year) or prolonged (>100 year) periods of establishment, while patterns of tree establishment in mid-aged (100 to 350 year) forests remains largely unknown. To determine temporal patterns of Douglas-fir establishment following stand-replacing fire, increment cores were obtained from 1455 trees in 18 mature and early old-growth forests in western Washington and northwestern Oregon, USA. Each of the stands showed continuous regeneration of Douglas-fir for many decades following initiating fire. The establishment period averaged 60 years (range: 32–99 years). These results contrast both with the view of rapid (one- to two-decade) regeneration of Douglas-fir promoted in the early forestry literature and with reports of establishment periods exceeding 100 years in older (>400 year) Douglas-fir–western hemlock stands. These results have important implications for management designed to create and promote early-seral forest characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".