Use of foliar calcium to strontium ratios to partition soil calcium sources of American beech on two sites in Southern Québec
Bibliographic record
Abstract
Forest ecosystems in southern Quebec have been exposed to high levels of atmospheric acid deposition for many decades. American beech (Fagus grandifolia Ehrh.), an acid-tolerant species, is increasing in abundance in the sugar maple (Acer saccharum Marsh.) dominated forest of southern Quebec. I therefore hypothesized that beech is better adapted than sugar maple to access the soil Ca in these forests. In my thesis, I partitioned the source of soil Ca by horizon/depth for beech foliage at two sites of contrasting soil fertility using a Ca/Sr approach. The discrimination of Ca over Sr by beech was determined by sampling seedlings and their extended rhizosphere (n = 40) on sites with large differences in Ca and Sr supply, as well as Ca/Sr ratios. With the discrimination function determined (R2=0.5, p<0.05), a study was conducted to determine if leaf Ca/Sr was independent of the height of sampling. Trees were sampled at three different heights (3, 6, and 13 m). Leaf Ca/Sr was found to be independent of the height of sampling in both sugar maple and beech (p = 0.67). Mature beech trees were sampled on a poor sandy ridge in the Morgan Arboretum (n = 22) and in the Hermine watershed in the Lower Laurentians (n =18). At both sites, soils were acidic with decreasing fertility with depth and low Ca/Al ratios. Beech was found to take up soil Ca primarily from the F horizon on both sites (> 60%) and the contribution from deeper soil horizons generally decreased with depth. The more superficial uptake of Ca by beech compared to sugar maple could provide it with a competitive advantage in terms of nutrition which could explain in part its increasing dominance in the Quebec landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".