Nutrient foraging via physiological and morphological plasticity in three plant species
Bibliographic record
Abstract
Physiological and morphological plasticity of roots enhance plant nutrient uptake in spatiotemporally heterogeneous soil environments. We examined these two types of plasticity using three plant species (Solidago altissima (L.) Raf., Pinus taeda L., and Liquidambar styraciflua L.). We grew plants in pots (one plant per pot) with equal quantity of fertilizer applied either evenly over the pot surface (H) or on one-quarter of the pot surface (T). A high-concentration 15N-labeled ammonium nitrate solution was injected twice over 48 h before harvest at a random location in H pots, and in either unfertilized or fertilized portion of T pots. Physiological plasticity of N uptake was observed in S. altissima and L. styraciflua. The highest 15N uptake rate for L. styraciflua occurred in H pots (medium level), and that for S. altissima occurred in fertilized portions of T pots (rich level). When low-concentration 15N was added to S. altissima, no differences in uptake were noted among treatments, possibly because of interroot competition. In S. altissima and P. taeda, either morphological or physiological plasticity was strong. In L. styraciflua, both types of plasticity were strong. Total 15N uptake was enhanced when 15N was added to the fertilized patches. Physiological plasticity contributed >70% of enhanced 15N uptake in S. altissima and L. styraciflua.
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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 | 0.000 |
| 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) | 0.001 | 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".