What makes a swamp swampy? Water regime and the botany of endangered wetlands in western Victoria
Bibliographic record
Abstract
Freshwater temporary wetlands are a little-studied ecosystem worldwide. They have been recognised as critically endangered in south-eastern Australia under Australian biodiversity conservation legislation. However, little has been recorded about their hydrology, functioning or biodiversity values; i.e. the factors that make them intrinsically ‘swampy’. In this paper, we developed a simple threshold model of wetland hydrology based on historical rainfall records and calculated evaporation records matched to records and recollections of the owners of swamps, and documented water-plant and microalgal species richness. The model indicated that swamps were inundated to at least 10-cm depth in an average of 6.3 years per decade for most of the 20th century. The average dry time between inundations was 1.27 years (maximum of 4.5 years). Since 1998, the frequency of inundation appears to have decreased, and the average dry times have increased. Despite, or because of, their temporary nature, these swamps have high biodiversity values among the vegetation and the microalgae, more than has been recorded for near-by permanent wetlands. There is no evidence that a drier and warmer climate will have a negative impact on biodiversity values; however, land management is likely to be important for maintaining these systems as the climate changes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".