Bibliographic record
Abstract
inherently inter-disciplinary, could bring to our understanding of COVID spread and its consequences.In addition to drawing on previous research by landscape epidemiologists, Azevedo et al. (2020) highlighted the need to draw on expertise on links between social and ecological systems, and research within urban landscapes and on ecological goods and services.These are areas that many landscape ecologists have been doing work in, but Azevedo et al. (2020) stressed the importance of these areas to respond to the COVID-19 pandemic.They highlighted the need for a landscape epidemiology approach to better link environmental and health research with a goal of increasing human and social systems.Their overall goal was to illustrate what landscape ecology as a discipline could learn from the COVID pandemic, and how the inherent interdisciplinarity, spatially explicit focus, and cross-scale perspective of our discipline could contribute to solutions to minimize disease spread and build a better world post-pandemic.The articles that comprise this Collection reflect many (but not all) of the themes highlights by Azevedo et al. (2020) early in the pandemic (https:// link.sprin ger.com/ colle ctions/ fbbjg hdeha).Emphasizing the ecological focus on landscape ecology, Azevedo et al. (2020) highlighted how concepts and tools from landscape ecology could aid in our understanding of how landscape change (including habitat loss and fragmentation, road building, disturbances, and climate change) could influence the spread of diseases.In this Collection,
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".