Diagnostic tools to evaluate the foliar nutrition and growth of hybrid poplars
Bibliographic record
Abstract
This 2-year study examined the effect of fertilizers on tree growth and foliar nutrition in a Populus trichocarpa Torr. & A. Gray × Populus deltoides Bartr. ex Marsh. plantation located in southwestern Québec. The treatments included a control that did not receive N or P fertilizer, inorganic NP fertilizers, organic fertilizers applied at 65–70 kg N·ha–1, and organic fertilizers applied at 130–140 kg N·ha–1. Fertilized trees were taller and had larger diameters than control trees. Three methods were used to diagnose limiting nutrients and nutrient imbalances, and compare the nutrient supply from different fertilizer sources. The critical value approach and the compositional nutrient diagnosis methods found below-optimum N and P concentrations, sufficient K and Mg concentrations, and an excessive Ca concentration in foliage. Vector analysis compared the N nutrition in foliage from fertilized trees and the control trees. The compositional nutrient diagnosis r2(nutrient imbalance index) was negatively correlated with annual tree growth in height (r = –0.46, P < 0.05) and diameter (r = –0.59, P < 0.05), meaning that trees with a greater nutrient imbalance grew less in height and diameter than trees with balanced foliar nutrition. Of these diagnostic methods, compositional nutrient diagnosis holds promise for identifying nutrient limitations and predicting growth responses to fertilization in hybrid poplar plantations.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".