Negative effects of fertilization on plant nutrient resorption
Why this work is in the frame
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Bibliographic record
Abstract
Plants in infertile habitats are thought to have a high rate of nutrient resorption to enable them reuse nutrients more efficiently than those in fertile habitats. However, there is still much debate on how plant nutrient resorption responds to nutrient availability. Here we used a meta-analysis from a global data set of 9703 observations at 306 sites from 508 published articles to examine the effects of nitrogen (N) and phosphorus (P) fertilization on plant foliar N and P concentrations and resorption efficiency. We found that N fertilization enhanced N concentration in green leaves by 27% and P fertilization enhanced green-leaf P by 73% on average. The N and P concentrations in senesced leaves also increased with respective nutrient fertilization. Resorption efficiencies (percentage of nutrient recovered from senescing leaves) of both N and P declined in response to respective nutrient fertilization. Combined N and P fertilization also had negative effects on both N and P resorption efficiencies. Whether nutrient resorption efficiency differs among plant growth types and among ecosystems, however, remains uncertain due to the limited sample sizes when analyzed by plant growth types or ecosystem types. Our analysis indicates that fertilization decreases plant nutrient resorption and the view that nutrient resorption is a critical nutrient conservation strategy for plants in nutrient-poor environments cannot be abandoned. The response values to fertilization presented in our analysis can help improve biogeochemical models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it