Evaluating the use of Ca/Sr and <sup>87</sup>Sr/<sup>86</sup>Sr ratios to track Ca sources in sugar maple in Ontario
Bibliographic record
Abstract
Decades of acidic deposition and timber harvesting have depleted calcium (Ca) stocks in soils, especially at base-poor soils characterized by low base cation weathering rates. One approach to tracking Ca sources from soil is by using Ca/Sr ratios in vegetation, while 87Sr/86Sr ratios have also been used to estimate mineral weathering rates. To evaluate the uses of Ca/Sr ratios and Sr isotopes in identifying Ca sources in sugar maple ( Acer saccharum Marsh.) trees, three base-poor sites on the Canadian Shield and three limestone sites in southern Ontario were sampled for Ca, Sr, and 87Sr/86Sr ratios. Higher Ca/Sr ratios in soil extracts and sugar maple tissues at base-rich sites compared with base-poor sites reflect different minerology among regions, while the Ca/Sr discrimination factor between roots and foliage indicated that internal cycling exerts a major control on Ca/Sr ratios in sugar maple. At the three off-shield sites, 87Sr/86Sr ratios in soil and tree tissues were higher than precipitation but were indistinguishable for off-shield sites. Mixing models using a 1.0 mol L−1 HCl soil extract as the weathering endmember indicated that a lower proportion of weathering Ca compared with other geochemical approaches. One potential explanation is that the extraction method dissolves more recalcitrant minerals to a greater extent than under field conditions, leading to a higher weathering rate endmember value used in the mixing model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".