Biodiversity assessment: a case study in predicting richness from the potential distributions of plant species in the forests of south‐western Australia
Bibliographic record
Abstract
Abstract Distributions were predicted for 1430 native plant species as part of a biodiversity assessment in the forests of south‐west Western Australia. From these predicted distributions, an index of plant species richness was generated for the forest area. The most common predictors for distribution were found to be climatic surfaces incorporating some aspect of seasonality in temperature and precipitation. Although coarse, the index confirmed existing knowledge of areas of high biodiversity within the study area and introduced a new area, the Blackwood Plateau, for consideration as a conservation reserve with high species richness. An additional survey was conducted to sample actual species richness in 11 test sites. When actual richness was regressed against predicted richness, a significant correlation was obtained if both annuals and geophytes were excluded from the analysis. With refinement of the model and further data collection targeted at areas of low effort, the species richness index is proposed as a useful tool for conservation planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".