Conservation of the world’s mammals: status, protected areas, community efforts, and hunting
Bibliographic record
Abstract
Mammals are imperiled worldwide. Threats to terrestrial species are primarily from habitat loss or modification, and in some instances from commercial, illegal, or unregulated hunting. Terrestrial species are negatively affected throughout the tropics from deforestation. Threats to marine mammals are related to harvest, strikes in shipping lanes, pollution, and depleted levels of food resources. Hazards to marine species are pronounced in the North Atlantic Ocean, North Pacific Ocean, and oceans and seas flanking southeastern Asia. Protected areas designed to conserve mammals often are too small, too few, poorly delimited or isolated, and too unreliably supported. The new conservation science proposes that human livelihoods be considered alongside traditional preservationist perspectives. For conservation outside of protected areas to succeed, the protection of wild mammals and their habitats should result in benefit to local people, especially in rural or poor communities. Concerns about declining populations of large mammals in North America during the late 19th and early 20th centuries resulted in the institution of regulations that contributed to the recovery of many populations. Today, in North America and Europe, wild populations are thriving and legal hunting is allowed for a number of mammals, something that is less common in many developing countries, where illegal killing remains a threat to conservation. Nevertheless, populations of large mammals are resilient to regulated hunting because of density-dependent processes that result in increased reproduction, survival, and growth rates. Unfortunately, hunting is unregulated for cultural and economic reasons over much of the Earth. We are beginning to see effects of climate change and invasive species on risk of extinction for many species. The future of mammals, however, is entwined ultimately with the size, growth, and resource demands of the human population.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".