Long-term demographic trends in a fire-suppressed mixed-conifer forest
Bibliographic record
Abstract
In the western United States, forests are experiencing novel environmental conditions related to a changing climate and a suppression of the historical fire regime. Mixed-conifer forests, considered resilient to disturbance due to their heterogeneity in structure and composition, appear to be shifting to a more homogeneous state, but the timescale of these shifts is not well understood. Our objective was to assess the effects of climate and fire suppression on stand dynamics and demographic rates of an old-growth mixed-conifer forest in the Sierra Nevada. We used a Bayesian hierarchical analysis to quantify species and community rates of recruitment, growth, and mortality. Despite a warming climate, we found that stand density, basal area, and carbon have increased over 56 years. Fir recruitment and growth significantly exceeded the community-level median rates, whereas pine recruitment and growth was significantly lower than the community-level median rates. Shifts in species composition from a well-mixed stand to a more dense fir-dominated stand appear to be driven by low growth and recruitment rates of pines relative to firs. In forests such as these with consistent and relatively low mortality rates, we recommend that restoration and management activities be focused on promoting pine recruitment and growth.
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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 | 0.000 | 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) | 0.000 | 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".