Broad-scale environmental influences on the abundance of saltwater crocodiles (Crocodylus porosus) in Australia
Bibliographic record
Abstract
Saltwater crocodile (Crocodylus porosus) populations have recovered strongly across northern Australia over the 30 years since the species was protected from hunting. However, monitoring studies show large geographical variations in abundance across the Northern Territory, Queensland and Western Australia. The Northern Territory has considerably higher densities, raising questions about constraints on recovery in the other states. We examined broad-scale environmental influences on population abundance by modelling the species–environment relationships across northern Australia. The hypothesis-based models showed strong support for the linkage to (1) the ratio of total area of favourable wetland vegetation types (Melaleuca, grass and sedge) to total catchment area, (2) a measure of rainfall seasonality, namely the ratio of total precipitation in the coldest quarter to total precipitation in the warmest quarter of a year, and (3) the mean temperature in the coldest quarter of a year. On the other hand, we were unable to show any clear negative association with landscape modification, as indicated by the extent of high-impact land uses or human population density in catchments. We conclude that geographical variations in crocodile density are mostly attributable to differences in habitat quality rather than the management regimes adopted in the respective jurisdictions.
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 | 0.001 |
| 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".