Overcoming physiological dormancy in seeds of <i>Triodia</i> (Poaceae) to improve restoration in the arid zone
Bibliographic record
Abstract
Reinstating dominant Triodia grassland communities following disturbance has been a focus of arid land restoration practitioners for decades in Australia. Yet, seed quality and variable seed germination have seriously hindered the reestablishment potential of Triodia species to date. This study set out to examine diaspore quality, germination requirements, and seed dormancy in seven Triodia species to identify first, then resolve, germination impediments. Freshly collected florets from all species were cleaned to ensure that each floret contained a viable seed and then evaluated for their initial germination capacity. Very low germination from florets (<10%) indicated the presence of physiological seed dormancy ( PD ) in all species. However, germination was significantly improved (up to 57%) with the use of 0.67 µm karrikinolide ( KAR 1 ), and to a lesser degree with 289 µm gibberellic acid ( GA 3 ). When the covering floret structures (i.e. lemma and palea) were removed, germination increased up to 59%, which was further enhanced after exposure to GA 3 (up to 66%) and KAR 1 (up to 92%). Optimal germination temperatures varied from 20 to 35°C and were species specific. Dry after‐ripening ( DAR −30°C and 50% relative humidity [ RH ]) of florets and seeds promoted the progressive loss of PD over 12–24 months storage for most species. Germination, dormancy level, and response to incubation temperature differed amongst species, experimental units (florets and seeds), DAR treatments, and after exposure to germination stimulants ( GA 3 and KAR 1 ). For use in restoration seeding programs, careful consideration of seed pre‐treatments is necessary to improve germination in Triodia species.
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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 | 0.000 | 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".