National Red Lists: the largest global market for IUCN Red List Categories and Criteria
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 6:185-191 (2008) - DOI: https://doi.org/10.3354/esr00143 The path from grey literature to Red Lists N. Mrosovsky1, M. H. Godfrey2,3,* 1Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, M5S 3G5 Toronto, Ontario, Canada 2North Carolina Wildlife Resources Commission, 1507 Ann Street, Beaufort, North Carolina 28516, USA 3Nicholas School of Environment and Earth Sciences, Duke University Marine Lab, 135 Marine Lab Road, Beaufort, North Carolina 28516, USA *Corresponding author. Email: mgodfrey@seaturtle.org ABSTRACT: This paper concerns the process by which Red List designations are decided and supported; it does not concern whether the past or present Red List categorizations are correct. We argue that, contrary to statements extolling the scientific and authoritative nature of the Red List, the reality for some species falls far short of these ideals. The prominent role played by the grey literature is an important factor in these problems. We use the case of the hawksbill turtle Eretmochelys imbricata as an example of the problems with relying on unavailable grey literature, but similar problems apply to various taxa classified in the Red List. KEY WORDS: IUCN · Red List · Grey literature · Citations · Transparency Full text in pdf format PreviousNextCite this article as: Mrosovsky N, Godfrey MH (2008) The path from grey literature to Red Lists. Endang Species Res 6:185-191. https://doi.org/10.3354/esr00143 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".