A preliminary assessment of the role of aquarium plant trade in the spread of non-native aquatic plants in South Africa: A DNA barcoding approach
Bibliographic record
Abstract
Although an ever-increasing human population drives the push for plant sciences to ensure global food security, it is the threat of a lack of adequate supplies of fresh water that delivers the greatest sense of urgency to the issue.Drought and its affects on agricultural production hinder global efforts to deliver food security and the current use of almost 70% of the planet's fresh water for irrigation is not sustainable.The response of plant science is to develop new strategies to produce crops that either use less water or can remain productive under drought.Understanding how plants respond to dehydration is an important prerequisite.Our approach is to target resurrection plants for uncovering genes/processes involved in dehydration tolerance and offer novel strategies for crop improvement.We have used "omic" technologies to develop tools and highly informative analyses of the response of the C4 African resurrection grass Sporobolus stapfianus, to dehydration.To determine which genetic components and cellular processes have a role in tolerance we have employed an evolutionary approach.Through integration of "omics" data within a phylogenetic framework, we generated novel insights and hypotheses regarding the mechanisms of dehydration and desiccation tolerance in S. stapfianus.Our current emphasis on the comparison between shoot and root responses to dehydration in both the tolerant S. stapfianus and its sensitive sister species S. pyramidalis, have generated insights into the role of oxidative stress management, nitrogen metabolism/remobilzation, and carbohydrate reallocation in dehydration tolerance.The interplay between the transcriptome and the metabolome in the establishment and response to dehydration in contrasting species generates an evolutionary informed systems-level understanding of how plants respond to dehydration leading to desiccation tolerance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".