Vegetation responses to stand structure and prescribed fire in an interior ponderosa pine ecosystemThis article is one of a selection of papers from the Special Forum on Ecological Studies in Interior Ponderosa Pine — First Findings from Blacks Mountain Interdisciplinary Research.
Bibliographic record
Abstract
A large-scale interior ponderosa pine ( Pinus ponderosa Dougl. ex P. & C. Laws.) study was conducted at the Blacks Mountain Experimental Forest in northeastern California. The primary purpose of the study was to determine the influence of structural diversity on the dynamics of interior pine forests at the landscape scale. High structural diversity (HiD) and low structural diversity (LoD) treatments were created with mechanical thinning on 12 main plots. Each plot was then split in half with one-half treated with prescribed fire. During the 5 year period after the treatments, the LoD treatments showed slightly higher periodic annual increments for basal area (BA) and significantly higher diameter increments than did the HiD treatments, although HiD carried twice as much BA as LoD did immediately after the treatments. Prescribed fire did not affect growth, but killed and (or) weakened some trees. No interaction between treatments was found for any variable. Stand density was reduced from the stands before treatments, but species composition did not change. Old dominant trees still grew and large snags were stable during the 5 year period. Treatments had minor impacts on shrub cover and numbers. These results suggest that ponderosa pine forest can be silviculturally treated to improve stand growth and health without sacrificing understory shrub diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".