Data from: Spending limited resources on de-extinction could lead to net biodiversity loss
Bibliographic record
Abstract
There is contentious debate surrounding the merits of de-extinction as a biodiversity conservation tool. Here, we use extant analogues to predict conservation actions for potential de-extinction candidate species from New Zealand and the Australian state of New South Wales, and use a prioritization protocol to predict the impacts of reintroducing and maintaining populations of these species on conservation of extant threatened species. Even using the optimistic assumptions that resurrection of species is externally sponsored, and that actions for resurrected species can share costs with extant analogue species, public funding for conservation of resurrected species would lead to fewer extant species that could be conserved, suggesting net biodiversity loss. If full costs of establishment and maintenance for resurrected species populations were publicly funded, there could be substantial sacrifices in extant species conservation. If conservation of resurrected species populations could be fully externally sponsored, there could be benefits to extant threatened species. However, such benefits would be outweighed by opportunity costs, assuming such discretionary money could directly fund conservation of extant species. Potential sacrifices in conservation of extant species should be a crucial consideration in deciding whether to invest in de-extinction or focus our efforts on extant species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".