JARA: ‘Just Another Red-List Assessment’
Bibliographic record
Abstract
Abstract Identifying species at risk of extinction is necessary to prioritise conservation efforts. The International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species is the global standard for quantifying extinction risk, with many species categorised according to population reduction thresholds. We introduce the Bayesian state-space framework ‘JARA’ (Just Another Red-List Assessment). Designed as decision-support tool, JARA allows both process error and uncertainty to be incorporated into IUCN Red List assessments under criterion A. JARA is implemented via an R package that is designed to be easy to use, rapid and widely applicable, so conservation practitioners can apply it to their own count or relative abundance data. JARA outputs display easy to interpret graphics of the posterior probability of the population trend against the corresponding IUCN Red List categories, as well as additional graphics to describe the timeseries and model diagnostics. We illustrate JARA using three real-world examples: (1) relative abundance indices for two elasmobranchs, Yellowspotted Skate Leucoraja wallacei and Whitespot Smoothhound Mustelus palumbes ; (2) a comparison of standardized abundance indices for Atlantic Blue Marlin Makaira nigricans and (3) absolute abundance data for Cape Gannets Morus capensis . Finally, using a simulation experiment, we demonstrate how JARA provides greater accuracy than two approaches commonly used to assigning a Red List Status under criterion A. Tools like JARA can help further standardise Red List evaluations, increasing objectivity and lowering the risk of misclassification, with substantial benefits for global conservation efforts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".