Native seed for restoration: a discussion of key issues using examples from the flora of southern Australia
Bibliographic record
Abstract
Land clearing across southern Australia since European settlement has fundamentally changed the amount and distribution of native vegetation; it has also substantially reduced genetic diversity in plant species throughout Australia, especially in agricultural regions. The most recent State of the Environment report indicates that Australian biodiversity continues to decline. Many approaches to restoration are used in Australia including re-establishing plant populations using tube stock or by direct seeding. Native seed for these projects is often assumed to be plentiful and available for the majority of species we wish to restore but these assumptions are rarely true. We also rely on a small number of species for the majority of restoration projects despite the vast number of species required to fully restore complex plant communities. The majority of seed for restoration is still primarily collected from native vegetation despite longstanding concerns regarding the sustainability of this practice and the globally recognised impacts of vegetation fragmentation on seed production and genetic diversity. Climate change is also expected to challenge seed production as temperatures rise and water availability becomes more limited; changes to current planting practices may also be required. Until now native seed collection has relied on market forces to build a strong and efficient industry sector, but in reality the Australian native seed market is primarily driven by Federal, State and Territory funding. In addition, unlike other seed-based agri-businesses native seed collection lacks national industry standards. A new approach is required to support development of the native seed collection and use sector into an innovative industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".