A database of schemes that prioritize sites and species based on their conservation value: focusing business on biodiversity
Bibliographic record
Abstract
BACKGROUND: Biodiversity offsets are conservation projects used mainly by business to counterbalance the environmental impacts of their operations, with the aim of achieving a net neutral or even beneficial outcome for biodiversity. Companies considering offsets need to know: (1) if there are areas of such biological importance that no impact is acceptable, and outside of these no-go areas, (2) the relative importance of biodiversity in the impacted site versus the site(s) proposed for protection, to ensure that the offset is of equal or greater status than that lost through the company's operations. We compiled a database of 40 schemes that use various methods to assess conservation priorities, and we examined if the schemes would allow companies to answer the above questions. DESCRIPTION: Overall, schemes tend to be designed to guide conservation organizations in their own priority setting or they categorize species based on conservation status. Generally, the schemes do not provide all the necessary information for offsets because they operate at a broad spatial scale or with low spatial resolution, which make it difficult to assess sites at the project level. Furthermore, most schemes do not explicitly incorporate threat, which we consider key to assessing whether offsets protect habitats or species that would otherwise be lost (i.e., provide additionality). The schemes are useful, however, for identifying the major conservation issues in different ecosystems around the globe. CONCLUSION: Companies can proceed by first avoiding, reducing, and mitigating impacts, and then using existing schemes to identify i) no-go areas and ii) appropriate offsets to compensate for any unavoidable loss in biodiversity. If existing schemes are inadequate, then companies should use integrated conservation planning techniques to define offset options within the region of their operations.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".