Sapling size influences shade tolerance ranking among southern boreal tree species
Bibliographic record
Abstract
1 Traditional rankings of shade tolerance of trees make little reference to individual size. However, greater respiratory loads with increasing sapling size imply that larger individuals will be less able to tolerate shade than smaller individuals of the same species and that there may be shifts among species in shade tolerance with size. 2 We tested this hypothesis using maximum likelihood estimation to develop individual-tree-based models of the probability of mortality as a function of recent growth rate for seven species: trembling aspen, paper birch, yellow birch, mountain maple, white spruce, balsam fir and eastern white cedar. 3 Shade tolerance of small individuals, as quantified by risk of mortality at low growth, was mostly consistent with traditional shade tolerance rankings such that cedar > balsam fir > white spruce > yellow birch > mountain maple = paper birch > aspen. 4 Differences in growth-dependent mortality were greatest between species in the smallest size classes. With increasing size, a reduced tolerance to shade was observed for all species except trembling aspen and thus species tended to converge in shade tolerance with size. At a given level of radial growth larger trees, apart from aspen, had a higher probability of mortality than smaller trees. 5 Successional processes associated with shade tolerance may thus be most important in the seedling stage and decrease with ontogeny.
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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 | 0.000 | 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".