The aquatic plant communities of the Pilbara region of Western Australia: a region of arid zone wetland diversity
Bibliographic record
Abstract
Context Decision making in conservation depends on robust biodiversity data. Well-designed systematic and rigorous surveys provide consistent and taxonomically broad datasets needed for conservation planning. This is important in areas such as the Pilbara of Western Australia with extensive mining and pastoralism. The collection of biodiversity data for aquatic plants represents a major contribution to assist in conservation planning and management of the region’s wetlands and rivers. Aims We documented the diversity and major patterns in the aquatic flora of Pilbara wetlands and rivers, to provide data to inform conservation planning and manage impacts of major land uses such as mining and pastoralism. Methods We undertook a systematic quadrat-based survey of the aquatic flora of 98 Pilbara wetlands and rivers. The full range of wetland types was sampled. Composition of charophytes and vascular aquatic plant communities were analysed against wetland permanence and water body type. Key results A diverse aquatic flora with several novel taxa was discovered. Charophytes were a major component of the aquatic flora. Floristic composition was strongly related to wetland type and water permanence with permanent sites showing higher richness. Less permanent sites captured a distinct component of the Pilbara aquatic flora. Conclusions The aquatic flora of the Pilbara represents a significant component of the region’s biodiversity. Patterning was concordant with previous studies of the riparian plant communities and aquatic invertebrates of the region providing synergies in reserve system design and management efforts. Implications High quality spatial biodiversity data particularly for poorly surveyed regions or biotic groups can provide major insights critical for effective conservation planning and management.
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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 | 0.001 | 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".