Bibliographic record
Abstract
The era in which we now live has been called the anthropocene (Steffen et al. 2007), suggesting that humans have become such a global force that we fundamentally alter global ecological interactions, the carbon and nitrogen cycles (Gruber and Galloway 2008), the ecology of infectious diseases (Daszak et al. 2000), and our own climate. Such anthropogenic disturbance is often seen as an external perturbation rather than as a part of ecological systems. I take the alternate view that humans are just another strongly interacting component within the larger community of species. By putting humans back into community ecology, I explore the impacts of human predation on wildlife, and the consequences of predator community restructuring on human disease.In some cases, management actions informed by science can mitigate or reverse negative anthropogenic environmental impacts. For example, the scientific discovery of the ozone hole (Solomon 1988) led to international action to regulate ozone depleting chemicals. It is my goal as a scientist to provide fundamental ecological insight that can inform management. As a result, this work is broken into three policy-relevant research themes. The first research theme quantifies the impact of varying levels of human predation of pacific salmon on ecosystems. The second theme addresses how changes in predator communities influenced the emergence of Lyme disease and other tick-borne pathogens that `prey' on humans. The third research theme addresses the need to understand the impact of human predation on large primates in tropical forests.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".