Short- and long-term nutritional effects and retention of potassium derived from fertilizer in a sugar maple stand in southern Quebec
Bibliographic record
Abstract
The short-term (1–3 years) and long-term (23 years) effects of liming combined with potassium (K) fertilization on forest nutrition and K cycling were examined in a sugar maple (Acer saccharum Marsh.) stand in southern Quebec. Sugar maple leaves were sampled annually from 1988 (prefertilization year) to 1991 and in 2011–2012. Ten understory plant species, sugar maple sapwood, and soils were also sampled in 2012 and analyzed for K, calcium (Ca), magnesium (Mg), and rubidium (Rb). The recovery of fertilizer K was determined using the Rb/K reverse tracer method. Fertilization neither increased growth nor maintained higher sugar maple leaf K levels over the long term; however, leaf K to Ca and K to Mg ratios were still higher in fertilized plots than in control plots in 2012. The percentage of leaf K derived from fertilizer peaked 3 years after fertilization (36% ± 5%) and was down to 1989 levels by 2012 (15% ± 6%). Understory vegetation generally showed no differences in leaf K concentration between treatments, but percent K from fertilizers was greater than 25% in several species. There was no significant effect of fertilization on soil K, Ca, and Mg availability by 2012. Our results suggest that significant amounts of fertilizer K are still present on the site after 23 years but that base cation levels in leaves and upper soil horizons have returned to near prefertilization levels except for a slight enrichment in K. Although small, the effects of fertilization with liming on soil fertility and plant nutrient status in a maple stand can be long lasting.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".