Variation in seed softening patterns and impact of seed production environment on hardseededness in early-maturing genotypes of subterranean clover
Bibliographic record
Abstract
This paper describes variation in the dynamics of seed softening (loss of impermeability) in 20 early-maturing genotypes, including 6 cultivars, of subterranean clover (Trifolium subterraneum L.). It reports the effect of 3 sites of seed production in south-western Australia on the pattern of softening in the first summer–autumn and on total softening over the subsequent 2 years. Seeds were softened at a single field location and in a diurnally fluctuating cabinet (60°C/15°C). There was significant variation among genotypes in the pattern of seed softening over the first 5 months after senescence. Cultivars Nungarin, Dwalganup, and Geraldton softened most rapidly in late February, whereas cultivars Dalkeith, Urana, and Izmir softened most rapidly in late March. The duration of field exposure required in order for 50% of the first season’s softening to occur ranged from 44 to 108 days among the 20 genotypes. Persistence of hard seeds into the second and third years also varied among genotypes. Of the cultivars, Nungarin and Izmir had the highest levels of residual hard seed after 30 months (5.3% and 3.9%, respectively), whereas Dalkeith had the lowest (0.9%). Site of seed production had a small but significant effect on both the pattern of softening in the first summer–autumn and the persistence of hard seeds in subsequent years. Seeds produced in a relatively high-rainfall site (768 mm of growing-season rainfall plus supplementary irrigation) had a slower rate of hard seed breakdown than those from either of 2 sites located in the wheatbelt (217 and 423 mm growing-season rainfall). Seed softening through exposure in the field and in a 60°C/15°C fluctuating-temperature cabinet was compared for all genotypes. The cabinet treatment was fairly successful in ranking genotypes for relative between-season hardseededness, although it underestimated total softening by an average of 16%. However, the cabinet treatment was a poor predictor of the within-season pattern of seed softening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".