Bibliographic record
Abstract
Resources continues as a leader in publication of broad resources for the molecular ecology community.Molecular Ecology Resources has ranked highly (> 90th percentile) for several years in categories of Evolutionary Biology and Ecology based on impact factor and additional indicators.The current impact factor (IF) for MER is 5.5, which is lower than previous years.However, other impact metrics that are less sensitive to quirks introduced by the new calculation of IF provide a more consistent picture for MER such as observed with the 5-year IF (7.8 compared to 8.0 in previous year), h5index (66 which is the same as the previous year) and Scopus (CiteScore = 15.6 compared to 12.9 in previous year).The journal continues to navigate changes in the publishing landscape that includes new competition for quality papers, more options for open access, and improvements in the peer-review process.Ultimately, we strive to provide high-quality resources for the community with rigorous publication ethics and standards.Further, we aim to support our authors, reviewers and readers with responsive interaction and a high-quality experience.
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.235 | 0.186 |
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".