A technique to estimate the pre-fire depth of burial of Grevillea seeds by using seedlings after fire
Bibliographic record
Abstract
In a glasshouse experiment, we used the shrub Grevillea speciosa to examine the reliability of estimating the depth of seed burial based on the distance from the soil surface to the junction of the swollen hypocotyl with the radicle. We then examined the applicability of the technique in the field by using post-fire seedling emergence. We found that the mean length of the swollen hypocotyl in seedlings was a good predictor of the depth of seed burial (R2 = 0.97). Most variation occurred for seeds buried near the surface at 2-cm depth, where the swollen hypocotyl overestimated the depth of seed burial by about 1 cm. There was a decline in the ability of seeds to successfully emerge from soil depths below 2 cm, with seedlings of G. speciosa able to emerge from soil depths up to 8 cm in the glasshouse. This corresponds with the estimated maximum emergence depth based on seed mass. In the field, seedlings were estimated to have emerged from depths of 1–7 cm after an intense wildfire. There was an approximately normal distribution of seedling emergence depths; however, the mode of this distribution varied among locations. The technique gave comparable results to a similar technique developed for Acacia spp., allowing consideration of a greater range of species and post-fire locations. Use of the technique has the potential to provide insights into the impacts of fires below ground, including the nature of post-fire germination, residual soil seed banks, the depth-related action of fire-induced germination cues and small-scale spatial variation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".