Predicting Al, Cu, and Zn concentrations in the fine roots of trembling aspen (<i>Populus tremuloides</i>) using bulk and rhizosphere soil properties
Bibliographic record
Abstract
We compared the predictions of Al, Cu, and Zn concentrations in fine roots of trees using properties of the bulk and rhizosphere soils to find the best approach to assess the ecological risks of metals to trees. Predictions were made from 18 trembling aspens ( Populus tremuloides Michx.) equally distributed on six sampling sites using multiple linear regressions with soil Al, Cu, and Zn speciation data and chemical and microbial properties as explanatory variables. Fine root Al, Cu, and Zn concentrations ranged, respectively, from 2137 to 7480, from 7.03 to 206, and from 41.2 to 360 µg·g dry root mass–1. No significant prediction was obtained for Al. The soil total water-soluble Cu and Zn concentrations better predicted the metal concentrations in fine roots than the concentration of labile metal species. The concentrations of reactive Zn, water-soluble organic C, and NH4+ together with microbial biomass of N were the other significant explanatory variables. The best regression models for Cu and Zn were obtained in the rhizosphere and explained, respectively, 79.2% and 95.7% of the variation of metal concentrations in fine roots. This work pointed out that the rhizosphere properties and processes need to be considered to correctly assess the ecological risks of metals to tree.
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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 | 0.000 |
| 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 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".