Regional patterns of mammal abundance and their relationship to landscape variables in eucalypt woodlands near Darwin, northern Australia
Bibliographic record
Abstract
Habitat loss and fragmentation are usually construed as having negative consequences for wildlife, and habitat heterogeneity as having a positive effect. We conducted a mammal survey in eucalypt woodlands near Darwin, and found very few mammals in an intact region of the study area. This is consistent with an emerging pattern suggesting that many mammal species are declining across northern Australia, even though habitats remain relatively intact. However, we also found apparently healthy populations of the same species in a fragmented region of the study area. Using a combination of remote sensing, GIS and generalised linear modeling, we found some evidence of relationships between fire regime, fire heterogeneity or vegetation heterogeneity and the distributions of mammal species in this area. However, there was a strong regional component of the distribution that is not explained by these variables. The cause of the lack of mammals in the intact region of the study area has not been revealed by this analysis. One possible reason for this failure is that the landscape variables used in the analysis were too fine to detect variation in mammal abundance occuring at a much courser regional scale.
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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 | 0.000 |
| 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".