Transgenic Plants as Sensors of Environmental Pollution Genotoxicity
Bibliographic record
Abstract
Rapid technological development is inevitably associated with manyenvironmental problems which primarily include pollution of soil, water and air. In manycases, the presence of contamination is difficult to assess. It is even more difficult toevaluate its potential danger to the environment and humans. Despite the existence ofseveral whole organism-based and cell-based models of sensing pollution and evaluationof toxicity and mutagenicity, there is no ideal system that allows one to make a quick andcheap assessment. In this respect, transgenic organisms that can be intentionally altered tobe more sensitive to particular pollutants are especially promising. Transgenic plantsrepresent an ideal system, since they can be grown at the site of pollution or potentiallydangerous sites. Plants are ethically more acceptable and esthetically more appealing thananimals as sensors of environmental pollution. In this review, we will discuss varioustransgenic plant-based models that have been successfully used for biomonitoringgenotoxic pollutants. We will also discuss the benefits and potential drawbacks of thesesystems and describe some novel ideas for the future generation of efficient transgenicphytosensors.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".