Classification of direct threats to the conservation of ecosystems and species 4.0
Bibliographic record
Abstract
Identifying and assessing the magnitude of direct threats to ecosystems and species are critical steps to prioritizing, planning, implementing, and assessing conservation actions. Just as medical clinicians and researchers need a standard way to talk about human diseases, conservation practitioners and scientists need a common and comprehensive language to talk about the threats they are facing to facilitate joint action, evaluation, and learning. To meet this need, in 2008 the IUCN Species Survival Commission and the Conservation Measures Partnership produced the first version of a common threats classification with the understanding that it would be periodically updated to take into account new information and learning. We present version 4.0 of this classification. For this latest update, we reviewed existing versions and derivatives of the original classification, over 1000 citations of the classification, threats data from over 2900 real-world conservation projects, and comments from many users. Based on our findings, we made changes to the threats classification scheme, including addition of a level 0 threat class, refinement of levels 1 and 2 threat categories, and addition of the threat "Fencing & walls" to level 2. Also added were level 3 threat types and modifiers that provide a more detailed description of different types of direct threats and thus allow users to fine-tune analyses and actions. The update also clarifies how to treat various stressors, including natural disaster events and climate change. As a result of these changes, we revised the formal definition of direct threats. They include human actions that are the direct cause of ecosystem or species-population degradation and loss, such as agriculture, transport, natural resource use, and ecosystem management. They also include ultimate stressors in natural systems whose dynamics have been altered by the effects of current or historical human actions, such as invasive or problematic native species, pollution, natural disasters, and climate change.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".