Editorial 2026
Bibliographic record
Abstract
Heading into 2026, Molecular Ecology Resources remains a leading publication for broad resources in the field of molecular ecology.This is supported by various citation-based metrics, which continue to rank the journal highly in the related fields of Evolutionary Biology and Ecology.Our current Impact Factor (IF) of 5.5 places the journal 6th out of 53 in Evolutionary Biology and 21st out of 201 in Ecology in the Clarivate rankings.Other notable metrics additionally provide a positive picture for the journal, including the 5-Year IF (8.0 compared to 7.8 in the previous year) and Scopus CiteScore (15.2 compared to 15.6 in the previous year).During 2025, Molecular Ecology Resources has responded to the continuously changing publishing landscape, details of which are described in this editorial; however, our mission in 2026 remains the same, which is to publish highquality resources that are of broad impact and pertinence to the community.To achieve this goal, we strive to offer a rigorous review process while supporting our authors with a fair and responsive 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 | 0.005 | 0.035 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.212 | 0.174 |
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".