Effects of defoliation on growth, biomass allocation, and wood properties of<i>Betula pendula</i>clones grown at different nutrient levels
Bibliographic record
Abstract
Three-year old Betula pendula Roth clones were grown at two nutrient levels in a field experiment to investigate the responses and recovery in growth and wood properties to a range of defoliation levels (0100%). No general threshold value of defoliation level for negative effects in growth was found, since the sensitivity of saplings to defoliation varied according to plant traits studied. However, responses were related to defoliation intensity. Saplings compensated for 25% defoliation in terms of height growth and number of current branches and were able to tolerate 50% defoliation without effects on diameter growth 1 year after the defoliation. Nutrient availability was significant only in determining how total biomass responded to defoliation. Fertilized saplings were able to tolerate 25% defoliation without reduction in total biomass, but nonfertilized saplings were not. The interaction between defoliation and fertilization disappeared in the second growing season after the defoliation. Saplings were not able to compensate for 75% defoliation in terms of total biomass or for 100% defoliation in terms of growth and branching even in 2 years' recovery time. In stemwood, complete defoliation reduced growth ring width and vessel diameter simultaneously and also induced a narrow zone of secondary xylem with defects. Our results suggest that defoliation level and recovery time played a crucial role in compensatory growth of birch saplings, while nutrient availability had a minor role.
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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 | 0.001 | 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 | 0.001 |
| 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".