The immediate impact of 1080 aerial baiting to control wild dogs on a spotted-tailed quoll population
Bibliographic record
Abstract
In eastern Australia, the spotted-tailed quoll (Dasyurus maculatus) is the species thought to be most likely at risk from aerial baiting with compound 1080 to control wild dogs (Canis lupus familiaris and C. l. dingo). Although it is known that quolls occasionally die of 1080 poisoning, the broader impact on populations remains unresolved. We therefore assessed the impact of a regular aerial baiting campaign on a population of spotted-tailed quolls. Baiting with 1080 meat baits was conducted by the local Wild Dog Control Association and followed the same procedure as in previous years with the exception that the biomarker, rhodamine B, was added to the baits. Prior to the baiting, 36 quolls were trapped and fitted with mortality radio-collars; 31 of these collars were still functional at the time of baiting. Quolls were monitored from a helicopter and on the ground until retrapped 5–9 weeks after baiting. Transmitters were then removed and a sample of vibrissae was taken for rhodamine B analysis. Carcasses found were analysed for 1080. Predator numbers were assessed before and after baiting using track pads across trails. Among the initial 36 radio-collared quolls, nine mortalities were recorded during the course of the study (seven after baiting). Only one of the nine deaths could be directly attributed to 1080 poisoning. In addition, vibrissae from five of the 35 individuals sampled after baiting were marked with rhodamine B, indicating that these individuals had consumed bait, and survived. Consequently, mortality attributable to this particular aerial baiting campaign was low, apparently because few quolls ate bait and most of those that did survived. Track counts for predators indicated a significant decrease in dog and fox numbers after baiting. Cat activity remained unchanged and the number of quoll tracks increased.
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 | 0.001 |
| 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".