Reproductive output of ponderosa pine in response to thinning and prescribed burning in western Montana
Bibliographic record
Abstract
Thinning and thinning followed by prescribed fire are common management practices intended to restore historic conditions in low-elevation ponderosa pine ( Pinus ponderosa Dougl. ex P. & C. Laws.) forests of the northern Rocky Mountains. While these treatments generally ameliorate the physiology and growth of residual trees, treatment-specific effects on reproductive output are not known. We examined reproductive output of second-growth ponderosa pine in western Montana 9 years after the application of four treatments: thinning, thinning followed by spring prescribed fire, thinning followed by fall prescribed fire, and unthinned control stands. Field and greenhouse observations indicated that reproductive traits vary depending on the specific management treatment. Cone production was significantly higher in trees from all actively managed stands relative to control trees. Trees subjected to prescribed fire produced cones with higher numbers of filled seeds than trees in unburned treatments. Seed mass, percentage germination, and seedling biomass were significantly lower for seeds from trees in spring burn treatments relative to all others and were generally higher in trees from fall burn treatments. We show for the first time that thinning and prescribed-burning treatments can influence reproductive output in ponderosa pine.
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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 | 0.000 | 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) | 0.000 | 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".