Ecological Synthesis and Its Role in Advancing Knowledge
Bibliographic record
Abstract
Abstract Synthesis has become ubiquitous in ecology. Despite its widespread application to a broad range of research topics, it remains unclear how synthesis has affected the discipline. Using a case study of publications (n = 2304) from the National Center for Ecological Analysis and Synthesis compared with papers with similar keywords from the Web of Science (n = 320,000), we address several questions about the comparative impact of synthesis, the role of synthesis in driving key research themes, and whether synthesis is focused on different topics than is the broader ecological literature. We found much higher citation rates for synthesis papers overall (fivefold more) and within eleven key topic themes (e.g., species richness, biodiversity, climate change, global change). Synthesis papers often played key roles in driving, redirecting, or resolving core questions and exhibited much greater cross-theme connectivity. Together, these results indicate that synthesis in science has played a crucial role in accelerating and advancing ecological knowledge.
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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 | 0.416 | 0.584 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.034 | 0.034 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".