Editorial 2026
Bibliographic record
Abstract
MetricsEcology of Freshwater Fish (EFF) is committed to The Declaration on Research Assessment (DORA), recognising the need to improve the evaluation of researchers and the outputs of scholarly research (https:// sfdora. org/ ).Thus, EFF no longer presents the Impact Factor as a standalone metric.Instead, several different metrics demonstrating the quality and influence in the scientific community are presented below (Table 1).For information on these metrics, please see the Journal Metrics Overview | Wiley. | Open AccessThe global publishing landscape is evolving, and we are accelerating towards an Open Access world.As we navigate this change, we are pleased to highlight the remarkable growth in Open Access within EFF.Over the years, we have witnessed a steady rise in the publication of Open Access papers, with year-on-year Open Access growth reaching over 80% in 2023 (Figure 1).As shown in Figure 2, this growth is driven by Wiley's Transformational Agreements, with 95% of Open Access papers published in 2023 so far being funded through these agreements.The Transformational Agreements enable authors affiliated with participating institutions to publish primary research and review articles Open Access at no cost to the author.Instead, the fees are funded by the institution as agreed within the agreement.In 2024, Wiley reached a significant milestone with over 100 transformational agreements.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.254 | 0.183 |
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".