Redefining and mapping global irreplaceability
Bibliographic record
Abstract
Irreplaceability is a concept used to describe how close a site is to being essential for achieving conservation targets. Current methods for measuring irreplaceability are based on representative combinations of sites, giving them an extrinsic nature and exponential computational requirements. Surrogate measures based on efficiency (complementarity) are often used as alternatives, but they were never intended for this purpose and do not measure irreplaceability. Current approaches used to estimate irreplaceability have key limitations. Some of these are a result of the tools used, but some are due to the nature of the current definition of irreplaceability. For irreplaceability to be stable and useful for conservation purposes and to resolve limitations, irreplaceability measures should adhere to five axioms; baseline coherence, monotonic responsiveness, proportional responsiveness, intrinsic stability, and bounded outputs. We designed a robust method for measuring a site's proximity to irreplaceability that adheres to these requirements and used it to develop the first systematic global map of irreplaceability based on data for terrestrial vertebrates (n = 29,837 species, >1 million grid cells). At least 3.5% of land surface was highly irreplaceable, and 47.6% of highly irreplaceable cells were contained in 12 countries. More generous thresholds of irreplaceability flag greater portions of land surface that would still be realistic to protect under current global objectives. Irreplaceable sites should form a critical component of any global conservation plan and should be part of the UN Convention on Biological Diversity's post2020 Global Biodiversity Framework strategy, forming part of the 30% protection by 2030 target that is gaining support. The reliable identification of irreplaceable sites will be crucial to halting extinctions.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".