Informing silvicultural strategies for a climate-resilient species: initial insights from an incense-cedar (<i>Calocedrus decurrens</i>) spacing trial
Bibliographic record
Abstract
Incense-cedar ( Calocedrus decurrens (Torr.) Florin) is a drought-resistant conifer species native to the western United States that is host to few lethal pests and pathogens. Quantifying its survival and growth after planting is important for defining its potential place within future silvicultural prescriptions. Our objectives are to understand the impact of (1) spacing and herbivore protectors on seedling survival, (2) spacing on growing space occupancy after 19 years, and (3) spacing on individual tree and stand characteristics. The use of herbivory protection had a positive influence on seedling survival (96% survival with protection; 92% survival without protection). After 19 years, including drought conditions, 89% of planting spots were occupied by a live tree. Mean live crown ratios occurred across a narrow range, from 89.4 to 97.8, indicating a lack of differentiation at this phase of stand development. Wider spacing treatments resulted in the expected trade-off of larger individual tree sizes but lower stand-level volume. Because of persistent live crowns, thinning and pruning are potential strategies not only to reduce fire risk but also to improve timber quality. Our results also support the contention that incense-cedar is a species well-adapted to the anticipated future climate.
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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 | 0.003 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".