Applications of the IUCN Red List: towards a global barometer for plant diversity
Bibliographic record
Abstract
ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 6:127-135 (2008) - DOI: https://doi.org/10.3354/esr00135 AS WE SEE IT Applications of the IUCN Red List: towards a global barometer for plant diversity Neil Brummitt*, Steven P. Bachman, Justin Moat Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AB, UK *Email: n.brummitt@kew.org ABSTRACT: The scale of the global biodiversity crisis means that international efforts to identify, conserve and monitor threatened species must be carried out at a greater speed than ever before. Recent developments in information technology present an opportunity to speed up the production of species conservation assessments, and methods and prospects for this are discussed in the context of the work being conducted on plant assessments for the International Union for Conservation of Nature (IUCN) Sampled Red List Index. The need for an internationally agreed upon, comparable, standardised system such as the Red List is emphasised here, but ultimately more efficient techniques must be developed to supplement the existing approach if this is to be able to meet the global demand. KEY WORDS: IUCN · Red List · Plants · Sampled Red List Index · SRLI · Geographical Information System · GIS Full text in pdf format PreviousNextCite this article as: Brummitt N, Bachman SP, Moat J (2008) Applications of the IUCN Red List: towards a global barometer for plant diversity. Endang Species Res 6:127-135. https://doi.org/10.3354/esr00135 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 6, No. 2. Online publication date: December 30, 2008 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2008 Inter-Research.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".