Comparison of thinning and prescribed fire restoration treatments to Sierran mixed-conifer historic conditions
Bibliographic record
Abstract
Thinning and prescribed fire are widely used to restore fire-suppressed forests, yet there are few studies of their effectiveness in Sierran mixed-conifer forest. We compared stand conditions in replicated plots before and after a combination of thinning and burning treatments against a reconstruction of the same forest in 1865. The historical forest had 67 stems/ha (trees ≥5 cm DBH), equal percentages of shade-tolerant and -intolerant tree species, stems randomly distributed at the stand scale, and a flat diameter distribution across size classes. The pretreatment forest averaged 469 stems/ha, which comprised 84% shade-tolerant and 14% shade-intolerant species, were highly clustered, and had a reverse-J-shaped diameter distribution. Thinning treatments failed to approximate historical composition, spatial pattern, or diameter distribution. Treatments left too many small trees, removed too many intermediate-sized trees (50–75 cm DBH), and retained a reverse-J-shaped diameter distribution. Current old growth comprises fewer large trees than historical conditions, suggesting that treatments should retain more intermediate-sized trees to provide for future large-tree recruitment. Understory thinning with prescribed fire significantly reduced stem density and produced a spatial pattern closest to historical conditions. Mixed-conifer restoration needs thinning prescriptions that vary by species and flexible rather than rigid upper diameter limits to retain some trees in all size classes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".