Evolution in biodiversity policy – current gaps and future needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intensity and speed of human alterations to the planet's ecosystems are yielding our static, ahistorical view of biodiversity obsolete. Human actions frequently trigger fast evolutionary responses, affect extant genetic variation and result in the establishment of new communities and co-evolutionary networks for which we lack past analogues. Contemporary evolution interplays with ecological changes to determine the response of organisms and ecosystems to anthropogenic pressures. Examples on wild species include responses to harvest (e.g. fisheries, hunting, angling), habitat loss and fragmentation (e.g. genetic effects of isolation), biotic exchange (e.g. evolutionary responses to control measures), climate change (e.g. local adaptation and its interplay with dispersal processes) and the responses of endangered species to conservation measures. A review of international and EU biodiversity policies showed numerous opportunities for the integration of evolutionary knowledge, with the realistic prospect of improving their efficacy. Such opportunities should be extended to other sectoral policies of direct relevance for biodiversity - notably nature conservation, fisheries, agriculture, water resources, spatial planning and climate change. These avenues for improvement are, however, challenged by the low level of enforcement of biodiversity policies, linked to the nonbinding nature of most biodiversity-policy documents, and the decreasing representation of biodiversity in EU's research policy.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it