Toxicity versus accumulation for barley plants exposed to copper in the presence of metal buffers: Progress towards development of a terrestrial biotic ligand model
Bibliographic record
Abstract
Development of a terrestrial biotic ligand model (TBLM) for higher plants requires a root-Cu accumulation value that corresponds to the 50% inhibitory concentration (IC50). However, it is not yet known which of the two previously reported Cu-binding ligands on the root is associated with Cu toxicity. The aim of the present study was therefore to investigate the relationship between Cu binding to each ligand group and toxicity, so that the key toxicological site could be identified. To obtain accumulation and toxicity data that were not biased by limited Cu supply to the root, 2-d-old barley seedlings were exposed for 48 h to a range of free Cu ion activities (i.e., {Cu(2+)}) in simple exposure media buffered by nitrilotriacetic acid (NTA) or ethylenediaminetetraacetic acid (EDTA). Comparison of the amount of predicted root-bound Cu (calculated with the aqueous geochemical program PHREEQC) with root elongation data showed that toxicity likely resulted from Cu binding to low-affinity ligands, as the high-affinity ligands were approximately 99% saturated when a reduction in root elongation was first observed. For plants exposed to both NTA- and the EDTA-buffered {Cu(2+)} solutions, the root-Cu accumulation value corresponding to the IC50 was approximately 80 microg/g root dry weight, which is similar to the value obtained from previous work with wheat. The linear relationship between the amount of Cu bound to the low-affinity ligands and the percent root growth inhibition suggests that this relationship will be a robust predictor of Cu toxicity when incorporated into the TBLM, and applied to varied exposure scenarios. For the simple solutions used here, the TBLM-predicted and measured IC50 values were statistically indistinguishable.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".