Chelator production by <i>Deschampsia cespitosa</i> (L.) Beauv. in adaptive Ni/Cu hyper-tolerance derived from fields in the Sudbury region and lab assessment
Bibliographic record
Abstract
Plants possess a complex network of mechanisms to utilize and, if necessary, detoxify metals. Plants utilize constitutive basal tolerance mechanisms to maintain appropriate internal metal levels under normal conditions. However, adaptive hyper-tolerance mechanisms are used in order to tolerate excess metal exposure. The production of metal binding chelators could be one way to convey these tolerances. Chelator production of field and greenhouse-derived materials was investigated to determine any multi-metal hyper-tolerances in different populations of the grass Deschampsia cespitosa (L.) Beauv. Plant tissue was collected from metal-contaminated mine sites, and from specimens grown in metal exposure hydroponic experiments. The chelator metabolites from these samples were simultaneously analyzed using HPLC-tandem mass spectrometry. In the hydroponic grown grass, histidine was produced at high concentrations solely in the hyper-tolerant populations during metal exposure. In all of the populations, the responses of chelators were metal-specific, where levels of nicotianamine were at high concentrations during Ni exposure, and levels of phytochelatins were high during Cu exposure. Moreover, a similar pattern of chelator production was seen in the root specimens collected from mine sites contaminated with Ni and (or) Cu. Histidine was the strongest Ni chelator involved in adaptive hyper-tolerance, while constitutive basal tolerance to Ni and Cu was observed via the responses of nicotianamine and phytochelatin, respectively.
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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 | 0.000 | 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) | 0.001 | 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".