Carnivores’ contributions to people in Europe
Bibliographic record
Abstract
Human-carnivore relations in Europe have varied throughout history. Because of recent conservation efforts and passive rewilding, carnivore populations are recovering, which translates into more interactions with humans. Thus, unraveling these interactions as well as the multiple contributions carnivores provide to people is crucial to their conservation. We examined the literature conducted in Europe since 2000 and used the nature’s contributions to people (NCP) framework to identify factors that have shaped human-carnivore relations. To do so, we examined the state of scientific knowledge and relationships among types of NCP from carnivores, countries, and carnivore species; and between NCP, actors, and management actions. Results indicated that research has been oriented toward large carnivore species and their detrimental contributions to people. Further, the effectiveness of carnivore management strategies has only been evaluated and monitored in a limited set of all the research. To balance any negative views on carnivores, we suggest that the recognition of the duality of carnivores, as providers of both beneficial and detrimental contributions, should be included in EU conservation policies.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".