Rapid Identification of Acacia Species With Potential Salt Tolerance by Using Nuclear Ribosomal DNA Markers
Bibliographic record
Abstract
Use of saline lands for agroforestry relies primarily on plant species that have the trait of salinity tolerance, and also other economic and agronomic benefits. The selection of species, however, also needs to consider other key factors such as compatibility with existing flora, and potential for environmental benefits such as improved soil fertility or lowering of the water table in the case of dryland salinity. The testing of candidate species in particular environments needs substantial investments of costs and time. In this paper, a novel approach is presented for the rapid identification of potentially salt tolerant Acacia species, based on molecular phylogenetic analysis. The approach has been applied to four species groups, Acacia pendula, A. salicina, A. victoriae and A. stenophylla. The nuclear-encoded ribosomal DNA internal and external transcribed spacer (ITS and ETS) regions were used as markers, and phylogenetic analyses undertaken to identify closely related species that may share the salt tolerance traits. Such a methodology could be used to more rapidly identify candidate native species for agroforestry in salinity-affected regions and for preventing further expansion of salinity, thus assisting in biodiversity conservation.
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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 | 0.001 | 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".