Carbon allocation during defoliation: testing a defense-growth trade-off in balsam fir
Bibliographic record
Abstract
During repetitive defoliation events, carbon can become limiting for trees. To maintain growth and survival, the resources have to be shared more efficiently, which could result in a trade-off between the different physiological processes of a plant. The objective of this study was to assess the effect of defoliation in carbon allocation of balsam fir [Abies balsamea (L.) Mill.] to test the presence of a trade-off between allocation to growth, carbon storage, and defense. Three defoliation intensities [control (C-trees, 0% defoliation), moderately (M-trees, 41-60%), and heavily (H-trees, 61-80%) defoliated] were selected in order to monitor several variables related to stem growth (wood formation in xylem), carbon storage in stem and needle (non-structural soluble sugars and starch), and defense components in needles (terpenoids compound) from May to October 2011. The concentration of starch was drastically reduced in both wood and leaves of H-trees with a quasi-absence of carbon partitioning to storage in early summer. Fewer kinds of monoterpenes and sesquiterpenes were formed with an increasing level of defoliation indicating a lower carbon allocation for the production of defense. The carbon allocation to wood formation gradually reduced at increasing defoliation intensities, with a lower growth rate and fewer tracheids resulting in a reduced carbon sequestration in cell walls. The hypothesis of a trade-off between the allocations to defense components and to non-structural (NCS) and structural (growth) carbon was rejected as most of the measured variables decreased with increasing defoliation. The starch amount was highly indicative of the tree carbon status at different defoliation intensity and future research should focus on the mechanism of starch utilization for survival and growth following an outbreak.
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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) | 0.001 | 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".