Advances in wildlife ecology and the influence of Graeme Caughley
Bibliographic record
Abstract
Graeme Caughley produced substantial advances in our understanding of interactions between large mammalian herbivores and the environments they occupy. The strength of his work lay in the logical approach to answering fundamental questions. While his life work contributed to our understanding of animal population dynamics, it is in the application of his research and ideas that we have greatly advanced the science of conservation biology. Two central legacies of Caughley’s lifelong work are that an understanding of basic science leads to more appropriate management, and that underlying assumptions must be explicitly stated and tested. By arguing that efficient management of ecosystems requires an understanding of the underlying mechanisms, he moved forward the application of basic research to management. Future advances in wildlife conservation must focus on three aspects: (1) the rules for stability in ecosystems, and how humans cause instability; (2) the decline in native habitats, mostly from agriculture, and how to renew and reconstruct them while expanding threatened populations; and (3) how to breed species in captivity, and then reintroduce them as a last line of defence.
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 | 0.003 | 0.001 |
| 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 | 0.002 |
| 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".