The threatened Gouldian finch preferentially forages in prescribed burnt savannas
Bibliographic record
Abstract
Background The granivorous finches of Australia’s tropical savannas heavily rely on a sequence of perennial and annual grass seed production to feed throughout the year. An increase in late dry season wildfires has been suggested to detrimentally effect seed production sequence and has been attributed to poor physiological condition and a reduction in fitness of granivorous finches. Early dry season prescribed burning is an asset protection management strategy often implemented to reduce the incidence of late dry season wildfire, but has also been shown to improve the abundance and nutritional quality of grass seed Aims To assess whether the Gouldian finch (Chloebia gouldiae) preferentially used areas that were subject to early dry season prescribed burning over areas that were not burnt. Methods The creation of a landscape mosaic across the landscape with varying fire histories. Then the assessment of individual finch movement and site utilisation using VHF-radio telemetry transmitters, detected by an array of static receivers deployed across the landscape. Key results Finches significantly preferred to forage in areas burnt in the early season every 2–3 years. Conclusions The study demonstrates that early dry season prescribed burning creates preferred foraging habitat patches for Gouldian finches within savanna fire mosaics.
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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 | 0.001 | 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) | 0.002 | 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".