Effects of drought on weight, survival and breeding success of agile antechinus (Antechinus agilis), dusky antechinus (A. swainsonii) and bush rats (Rattus fuscipes)
Bibliographic record
Abstract
Extreme weather conditions, such as drought, significantly decrease the survival and breeding success of numerous species. Despite the frequent occurrence of such conditions in Australia, little is known about the effects of changing environmental conditions on the native small mammals. This study, conducted from 2002 to 2004, focussed on sympatric wild populations of the agile antechinus (Antechinus agilis), with more limited information on the dusky antechinus (A. swainsonii) and the bush rat (Rattus fuscipes). Bodyweights of agile antechinus before and during the breeding season were significantly lower in 2003 (drought) than in 2002 or 2004. Survival of female agile antechinus and the number of young per litter also decreased significantly during drought. In contrast, the dusky antechinus showed no difference in mean bodyweights between years, high survival rates of females and similar litter sizes in 2002 and 2003. There was also no difference in bodyweight of bush rats between years. Low rainfall was recorded during pregnancy and lactation in the agile antechinus, but rainfall was higher during pregnancy and lactation in the dusky antechinus. The survival and breeding success of the agile antechinus may have been adversely affected by a combination of interspecific competition, timing of the breeding season and severity of the drought.
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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 | 0.000 |
| 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 | 0.000 |
| 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".