Looking across Scales in Disease Ecology and Evolution
Bibliographic record
Abstract
AbstractOver the past few decades, it has become clear that ecological and evolutionary dynamics are influenced by processes operating across spatial and temporal scales. Processes that operate on small spatial scales have the potential to influence dynamics at much larger scales; for example, a change in the physiology of a primary producer can alter primary productivity in an ecosystem. Similarly, evolution-a process that historically was thought of as occurring at longer timescales-can influence ecological dynamics and vice versa. The importance of considering multiple scales is broadly true in ecology and evolution, and it is especially important for studies of disease ecology and evolution. Yet characterizing the scales at which individual studies operate is surprisingly challenging, as we (re)discovered while trying to characterize articles published in this journal over the past three decades. However, while it is difficult to determine where one scale ends and another begins, it is also clear that work that spans across a spectrum can yield insights that could not be gleaned from a narrower focus. To demonstrate this, we highlight studies previously published in this journal that show the value of working across scales. We then introduce the six articles that comprise this Focused Topic section. Together, these articles present systems, theory, and methods that provide important insights that could not have been obtained from studying a single scale in isolation.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".