Short-term effects of prescribed burning on radial growth of Douglas-fir trees in south central British Columbia
Bibliographic record
Abstract
Radial increment of Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) trees in uncut and partially cut and grazed and ungrazed dry cool Interior Douglas-fir forests near Isobel Lake in southern central British Columbia was measured for 2-year periods before and after prescribed burning in burned and unburned (control) areas. Parameters thought to influence the effects of prescribed burning on tree growth – characteristics of the tree and its immediate environment, adjacent surface and ladder fuels, and fire effects – were also measured. Burning caused a decrease in tree radial growth. The proportional change in radial tree growth was not related to degree of cutting or the presence of cattle grazing, but was negatively correlated with preburn radial growth, tree size, and also with some fire effect variables, particularly char height, suggesting that an increasing proportional decline in radial growth was associated with increasing tree size and with increasing fire severity. Soil nutrient (N, S, P, K, Mg or Ca) level was not a significant determinant of tree growth. The best prediction model could only explain 23% of the variation in the change in radial growth. Our results show that the effects of fire on Douglas-fir radial growth are complex and difficult to predict with the variables measured.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".