Why is a landscape perspective important in studies of primates?
Bibliographic record
Abstract
With accelerated deforestation and fragmentation through the tropics, assessing the impact that landscape spatial changes may have on biodiversity is paramount, as this information is required to design and implement effective management and conservation plans. Primates are expected to be particularly dependent on the landscape context; yet, our understanding on this topic is limited as the majority of primate studies are at the local scale, meaning that landscape-scale inferences are not possible. To encourage primatologists to assess the impact of landscape changes on primates, and help future studies on the topic, we describe the meaning of a "landscape perspective" and evaluate important assumptions of using such a methodological approach. We also summarize a number of important, but unanswered, questions that can be addressed using a landscape-scale study design. For example, it is still unclear if habitat loss has larger consistent negative effects on primates than habitat fragmentation per se. Furthermore, interaction effects between habitat area and other landscape effects (e.g., fragmentation) are unknown for primates. We also do not know if primates are affected by synergistic interactions among factors at the landscape scale (e.g., habitat loss and diseases, habitat loss and climate change, hunting, and land-use change), or whether landscape complexity (or landscape heterogeneity) is important for primate conservation. Testing for patterns in the responses of primates to landscape change will facilitate the development of new guidelines and principles for improving primate conservation.
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 | 0.051 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".