A new vegetation classification for Western Australia’s Two Peoples Bay Nature Reserve and its significance for fire management
Bibliographic record
Abstract
Context Vegetation mapping is subject to a diversity of approaches and lack of coordination, leading to low repeatability and predictive power in the species-rich flora of the Southwest Australian Floristic Region. Yet it has potential as a tool of use in fire management. Aims This project, extending over five decades, aimed to develop an authoritative vegetation classification and map plant fire responses at Two Peoples Bay Nature Reserve. Methods Using Muir’s classification approach, field surveys were conducted with aerial photography in hand. Thirty-three vegetation units were identified, described, mapped, and photographed. Defining attributes and taxa were identified for each unit. Key results Map, descriptions, and photographs detail forest, woodlands, mallee, scrub thickets, heath, wetlands, and granite communities on the Reserve. The forest, woodland, and shrublands were adequately classified and mapped. However, granite complex and mallee were least satisfactory, oversimplifying a rich diversity of vegetation types and habitats. Conclusions The Reserve may be divided for management into the central third of heath, shrublands, and low woodlands largely across the isthmus, the dunes and wetlands of the west with a greater diversity of vegetation types, and the eastern granite inselberg attaining 408 m with the most diverse vegetation types. The latter inselberg needs continued protection from fire and other disturbances. Greatest change in vegetation is seen in lowland landscapes where fire activity has also been pronounced. Implications Vegetation mapping has been a valuable aid for managers and fire planning, and for active comanagement with appropriate Aboriginal families.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".