Wildlife Research in a changing world
Bibliographic record
Abstract
Wildlife populations on both land and in the sea are under increasing pressure from both direct and indirect anthropogenic impacts. Threats to wildlife, such as over-exploitation, habitat loss, invasive species, infectious disease and climate change persist; yet in some situations, wildlife populations are the subject of management to reduce their unwanted impacts on economic, agricultural and conservation interests. The contrasting requirements of declining and overabundant populations, sometimes existing as part of the same ecosystem, mean that wildlife management needs to be viewed increasingly as part of an integrated social–ecological system. Moreover, solutions to wildlife-management problems may require new combinations of ecological knowledge, technological innovation and an understanding of the social and economic factors involved. The disciplinary scope of Wildlife Research will be broadening to reflect these changes. Wildlife Research has tended to focus on terrestrial systems in the past, but research approaches and findings are increasingly relevant across different environments, and consequently, we will also be expanding our coverage of freshwater and marine systems. We are planning to publish at least one Special Issue each year, to highlight new areas of research and the increasing internationalisation of wildlife management, and we welcome suggestions for these. We are also pleased to be able to offer fast-track publication of papers that contain results of high topical significance or policy relevance. We hope that the increased topical, disciplinary and geographical coverage of Wildlife Research will enhance its position as one of the leading international journals in applied ecology and environmental management.
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 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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".