An extinction-risk assessment tool for flora threatened by Phytophthora cinnamomi
Bibliographic record
Abstract
A risk-assessment tool was used to investigate the risk of extinction from disease caused by Phytophthora cinnamomi to 33 taxa from the Stirling Range National Park, Western Australia. Criteria used to score risk of extinction were the direct impact of P. cinnamomi on taxa, number of extant or extinct populations, percentage of populations infested by P. cinnamomi, proximity and topographical relationship of populations to P. cinnamomi, proximity of populations to tracks and the number of additional threatening processes. Direct impact scores were derived from mortality curves determined from the survival of taxa after soil inoculation with P. cinnamomi in a shade-house environment. On the basis of the total extinction risk score, nine taxa had a ‘very high’, five had a ‘high’, six a ‘moderate’, eight a ‘low’, four a ‘very low’ and one ‘no’ risk of extinction. Whereas the methodology confirmed the current threatened status of nine taxa, it also identified five taxa, not currently listed, to be at ‘high’ risk of extinction. Other threatening processes identified included fire, herbivory, aerial canker disease and climate change. These combine with P. cinnamomi to push taxa further towards extinction. Quantification of risk of extinction identifies taxa at risk and allows for prioritisation of management actions for currently threatened flora. This risk-assessment methodology combined glasshouse inoculation with habitat and ecological data, current in situ disease impact and proximity to disease and vectors, to enable a more comprehensive assessment of extinction risk and may be used in other areas with endemic flora threatened by P. cinnamomi.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".