Nutrient Retranslocation Response of <i>Picea mariana</i> Seedlings to Nitrogen Supply
Bibliographic record
Abstract
The hypotheses that retranslocation is controlled by soil nutrient availability or plant nutrient reserves were tested under field conditions for one growing season using nutrient‐loaded and non‐loaded (conventional) black spruce [ Picea mariana (Mill.) BSP] seedlings planted on a poor, medium, and rich fertility soil created by equivalent applications of 0, 200, and 400 kg N ha −1 , respectively. Growth and nutrient uptake increased with N supply, and was consistently higher in loaded than conventional seedlings, demonstrating the advantage of nutrient loading practices to accelerate seedling growth across the range of soil N tested. Compared to the poor soil, new shoot biomass of loaded seedlings increased by 34 and 134% on the medium and rich soils, suggesting loaded seedlings may be more efficiently transplanted on more fertile sites. Net retranslocation of N, P, and K increased by 569, 185, and 102% by nutrient loading in the nursery, supporting the hypothesis of translocation driven by the magnitude of plant nutrient reserves. However, net N retranslocation diminished with time due to root system expansion that promoted uptake and reduced the need for N redistribution. Net retranslocation of N (the most limiting nutrient) declined with soil N supply, but that of P and K were relatively independent of soil fertility. Increased N availability in the soil enhanced N accumulation in the plants but lowered N retranslocation. We conclude that higher net retranslocation of N on poor soils is a phenotypic adjustment by P. mariana seedlings to maximize N use at low availability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".