Deep subsoil nutrient uptake in potassium-deficient, aggrading <i>Pinus resinosa</i> plantation
Bibliographic record
Abstract
Growth of Pinus resinosa Ait. (red pine) on a potassium-deficient sandy soil at the Charles Lathrop Pack Demonstration Forest in Warrensburg, New York, is influenced by fine-textured lenses at 23 m below grade. A possible mechanism for an observed increase in surface soil potassium over time is nutrient uptake by red pine roots penetrating into these fine-textured, subsoil layers, and subsequent cycling of these nutrients between foliage and surface soil horizons. To test this hypothesis, we applied nutrient tracers directly to the deep subsoil and measured their uptake over several growing seasons: Strontium was applied in 1989 and 1993, while rubidium-free potassium (the Rb/K reverse tracer method) was applied only in 1993. Trees treated in 1989 had significantly greater concentrations of foliar and bud strontium than control trees, and trees treated only in 1993 also demonstrated significant uptake of potassium 2 years after treatment. These effects were present regardless of whether the trees had been surface-fertilized with potassium five decades earlier. The results demonstrate the importance of subsoil nutrient pools in forest ecosystem function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".