Fear of the human ‘super predator’ in native marsupials and introduced deer in Australia
Bibliographic record
Abstract
Recent experiments have demonstrated that carnivores and ungulates in Africa, Asia, Europe and North America fear the human 'super predator' far more than other predators. Australian mammals have been a focus of research on predator naiveté because it is suspected they show atypical antipredator responses. To experimentally test if mammals in Australia also most fear humans, we quantified the responses of four native marsupials (eastern grey kangaroo, Bennett's wallaby, Tasmanian pademelon, common brushtail possum) and introduced fallow deer to playbacks of predator (human, dog, Tasmanian devil, wolf) or non-predator control (sheep) vocalizations. Native marsupials most feared the human 'super predator', fleeing humans 2.4 times more often than the next most frightening predator (dogs), and being most, and significantly, vigilant to humans. These results demonstrate that native marsupials are not naïve to the peril humans pose, substantially expanding the taxonomic and geographic scope of the growing experimental evidence that wildlife worldwide generally perceive humans as the planet's most frightening predator. Introduced fallow deer fled humans, but not more than other predators, which we suggest may result from their being introduced. Our results point to both challenges concerning marsupial conservation and opportunities for exploiting fear of humans as a wildlife management tool.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".