Responses of black spruce (Picea mariana) and tamarack (Larix laricina) to flooding and ethylene
Bibliographic record
Abstract
Black spruce (Picea mariana (Mill.) BSP) and tamarack (Larix laricina (Du Roi) K. Koch) are the predominant tree species in the boreal peatlands of Alberta, Canada, where low nutrient availability, low soil temperature and a high water table limit their growth. Effects of flooding for 28 days on morphological and physiological responses were investigated in greenhouse-grown black spruce and tamarack seedlings in a growth chamber. Flooding reduced root hydraulic conductance, net assimilation rate and stomatal conductance, and increased water-use efficiency (WUE) and needle electrolyte leakage in both species. Although flooded black spruce seedlings maintained higher net assimilation rates and stomatal conductance than flooded tamarack seedlings, flooded tamarack seedlings were able to maintain higher root hydraulic conductance than flooded black spruce seedlings. Needles of flooded black spruce developed tip necrosis and electrolyte leakage after 14 days of flooding, and these symptoms were subsequently more prominent than in needles of flooded tamarack seedlings. Flooded tamarack seedlings exhibited no visible injury symptoms and developed hypertrophied lenticels at their stem base. Application of exogenous ethylene resulted in a significant reduction in net assimilation, stomatal conductance and root respiration, whereas root hydraulic conductivity increased in both species. Thus, although flooded black spruce seedlings maintained a higher stomatal conductance and net assimilation rate than tamarack seedlings, black spruce did not cope with the deleterious effects of prolonged soil flooding and exogenous ethylene as well as tamarack.
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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".