Influence of landscape on the apportionment of Ca nutrition in a Boreal Shield forest of Saskatchewan (Canada) using <sup>87</sup>Sr/<sup>86</sup>Sr as a tracer
Bibliographic record
Abstract
A 87Sr/86Sr tracer technique was used to apportion the supply of calcium (Ca) between atmospheric deposition and soil mineral weathering in a pristine Boreal Shield forest of northern Saskatchewan. To assess the impact of landscape variability on soil mineral weathering 87Sr/86Sr ratios, the watershed was divided into six study plots at low, middle, and high elevations along two toposequences - one consisting of mixed woods and the other consisting of black spruce. Apportionment analysis shows that none of the trees in the study plots depend entirely on soil mineral weathering as a source of Ca. Calcium pools in trees are shifted towards the atmospheric end-member (31-98%), probably because of the low soil mineral weathering fluxes combined with relatively high atmospheric deposition fluxes. These results need to be considered as the consequence of buildup and recycling of atmospherically derived Ca in the soil-vegetation system rather than the direct and large use of current atmospheric Ca inputs by the trees on an annualized basis. We also find that the trees in the high-elevation plots contain more Ca from atmospheric sources (84-98%) than trees in middle (31-86%) and low (37-82%) elevation plots. Two mechanisms are proposed to explain these results. First, if each plot is assumed to receive a constant supply of atmospherically derived Ca, then hillslope changes in soil mineral weathering rates are required to explain the changing proportions of atmospherically derived Ca cycling internally within each plot. Second, trees on hilltops may capture more dry aerosols than trees that are more shielded from atmospheric circulation in the valleys. Our work supports previous Ca cycling models in other forested ecosystems suggesting that much of the Ca requirement in trees is met via the flux from the atmosphere, but adds knowledge with regards to the role of topographical effects and forest canopy height as local factors that can influence the balance of Ca captured from atmospheric sources and released from soil mineral weathering sources. Key words: Boreal forest, calcium cycling, atmospheric deposition, soil mineral weathering, strontium isotopes, 87Sr/86Sr, calcium to strontium ratios, Sr/Ca; forest floor, tree calcium pools
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".