High predictability in introduction outcomes and the geographical range size of introduced Australian birds: a role for climate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary We investigated factors hypothesized to influence introduction success and subsequent geographical range size in 52 species of bird that have been introduced to mainland Australia. The 19 successful species had been introduced more times, at more sites and in greater overall numbers. Relative to failed species, successfully introduced species also had a greater area of climatically suitable habitat available in Australia, a larger overseas range size and were more likely to have been introduced successfully outside Australia. After controlling for phylogeny these relationships held, except that with overseas range size and, in addition, larger‐bodied species had a higher probability of introduction success. There was also a marked taxonomic bias: gamebirds had a much lower probability of success than other species. A model including five of these variables explained perfectly the patterns in introduction success across‐species. Of the successful species, those with larger geographical ranges in Australia had a greater area of climatically suitable habitat, traits associated with a faster population growth rate (small body size, short incubation period and more broods per season) and a larger overseas range size. The relationships between range size in Australia, the extent of climatically suitable habitat and overseas range size held after controlling for phylogeny. We discuss the probable causes underlying these relationships and why, in retrospect, the outcome of bird introductions to Australia are highly predictable.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it