Post-fire ephemerals and spinifex-fuelled fires: a decision model for bilby habitat management in the Tanami Desert, Australia
Bibliographic record
Abstract
Seed from post-fire ephemeral plants like Yakirra australiense can form an important component of the diet of the bilby, a threatened arid-dwelling bandicoot. An investigation was conducted to determine the factors affecting the production of Yakirra and fire reoccurrence in the Tanami Desert. A simple decision model was produced to assist managers decide where and when to burn and what to expect regarding Yakirra australiense growth following fire and rainfall. The information used in the model may be derived from existing spatial databases that document rainfall and fire history and could be used to map broad-scale temporal change in total vegetation cover and components of habitat suitability for the bilby. The season and magnitude of rainfall and time-since-fire were the most important variables in the production of Yakirra seed. Rainfall >300 mm within a 2-month period of intense-complete burns could result in >5% Yakirra cover, and >100 mm was required for >1% cover. Negligible Yakirra cover occurred in adjacent long-unburnt plots. Total ground cover and spinifex cover varied substantially in relation to mean annual rainfall, but not in relation to substrate type. Long-unburnt spinifex cover was mostly too sparse to carry a fire in the southern part of the Tanami and the growth of non-spinifex vegetation would be necessary to boost fuel loads sufficiently to carry a fire. Generally, more than 41% cover was required to carry a fire but this was also dependent on weather conditions. Managers should aim to burn in late spring or early summer to improve Yakirra production. Fire management to enhance habitat suitability for the bilby would be of most benefit in the northern part of the Tanami Desert where growth rate of vegetation is greater and there is little existing fire age heterogeneity.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".