Coping Mechanisms of Plants to Metal Contaminated Soil
Bibliographic record
Abstract
Metals such as cobalt (Co), copper (Cu), iron (Fe) and nickel (Ni) are essential for nor‐ mal plant growth and development since they contribute to the function of many en‐ zymes and proteins. However, metals can potentially become toxic to plants when they are present at high levels in their bioavailable forms (Hall, 2002). Phytotoxic levels of one or more inorganic ions in soil can be found in various parts of the world. These toxic sites occurred through natural processes or by anthropogenic effects. Naturally toxic soils include saline, acidic and serpentine soils, while anthropogenic polluted soils occur through mining activities, aerial fallout, and the run-off from galvanized sources of electricity pylons or motorway verges polluted by vehicle exhaust fumes (Bradshaw, 1984). The biochemical effect of metals on plants varies and the excess metal usually results in oxidative damage which affects their phenotype (Kachout et al., 2009)
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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) | 0.003 | 0.001 |
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".