Defining entities in the Acacia saligna (Fabaceae) species complex using a population genetics approach
Bibliographic record
Abstract
Traditional morphological taxonomic classification is problematic in the Acacia saligna (Labill.) H.L.Wendl. species complex. Reliable identification of entities within the species is essential due to its extensive use both in Australia and overseas, its propensity for weediness, and its ongoing development for use in agroforestry. We used a Bayesian analysis approach to assess genetic structure in populations across the species natural range and to define the natural distributions of various genetic entities. The results indicate that three highly divergent genetic entities are apparent in the A. saligna species complex with further fine-scale genetic subdivision present within two. The three primary genetic entities correspond to the informally described subsp. ‘saligna’ and subsp. ‘pruinescens’ combined, subsp. ‘stolonifera’, and subsp. ‘lindleyi’. Within this primary structure two further entities are apparent; one separating subsp. ‘saligna’/‘pruinescens’ into eastern and western populations and the other distinguishing north-western ‘lindleyi’ populations from the rest of that subspecies distribution. The north-western catchments may have been an important refugium for the species diversity. The results of the study will aid in breeding programs, conservation of natural populations and control of invasive populations of this taxon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".