Whither ecosystem health and ecological medicine in veterinary medicine and education.
Bibliographic record
Abstract
The traditional view that veterinary medicine deals with animal health, public health, and biomedical science, can be reasonably extended to include ecosystem health (1); or put collectively as “one health.” The application of the concept of health, therefore, ranges in scale from individuals to ecosystems. The emergence and importance of ecosystem health has been driven by the realization that dealing with animal and public health must occur in the context of a better understanding of ecosystem processes. The exponential growth and development of human society and the consequent enormous increase in connectedness among the earth’s ecosystems are degrading and compromising natural processes upon which life depends. It has been averred that biological security is the most important societal issue for the 21st century and that the veterinary culture and education have not adapted to this circumstance (2). A much stronger focus on ecosystem health in veterinary medicine would seem to be an essential step in rectifying this situation. The determinants of health and disease must be understood in the context of nested ecosystems that are connected with growing intensity by social, economic, biological, and physical links (Figure 1). Consequently, dealing with ecosystem connectedness is of paramount importance in the development of science, technology, ethics, and civil organizations needed for ecosystem health management to the extent this is possible, given the enormous complexity of the subject matter. Open in a separate window Figure 1 An illustration of the nested nature of ecosystems and the potential of their connectedness to impact the determinants of health and disease.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".