The impact of feral camels (Camelus dromedarius) on remote waterholes in central Australia
Bibliographic record
Abstract
The Katiti and Petermann Aboriginal Land Trusts (KPALT) in central Australia contain significant biological and cultural assets, including the World Heritage-listed Ulu?u-Kata Tju?a National Park. Until relatively recently, waterbodies in this remote region were not well studied, even though most have deep cultural and ecological significance to local Aboriginal people. The region also contains some of the highest densities of feral dromedary camels (Camelus dromedarius) in the nation, and was a focus area for the recently completed Australian Feral Camel Management Project. Within the project, the specific impacts of feral camels on waterholes were assessed throughout the KPALT. We found that aquatic macroinvertebrate biodiversity was significantly lower at camel-accessible sites, and fewer aquatic taxa considered ‘sensitive’ to habitat degradation were found at sites when or after camels were present. Water quality at camel-accessible sites was also significantly poorer (e.g. more turbid) than at sites inaccessible to camels. These results, in combination with emerging research and anecdotal evidence, suggest that large feral herbivores, such as feral camels and feral horses, are the main immediate threat to many waterbodies in central Australia. Management of large feral herbivores will be a key component in efforts to maintain and improve the health of waterbodies in central Australia, especially those not afforded protection within the national park system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".