Bibliographic record
Abstract
Since the launch of Evolutionary Applications in 2008, the journal has won the Association of Learned and Professional Society Publisher’s publishing award for the best new journal, has achieved an impact factor of 5.14, published many landmark special issues, and perhaps most importantly, has rapidly established itself as a highly respected and reputable journal, ranking among the top 10 journals in the field of evolutionary biology. As we enter our fifth year of publication, our main goal continues to be to publish the highest quality research in applied evolutionary biology. To achieve this goal, we encourage you to submit your best work that uses evolutionary biology to address pressing issues related to medicine, climate change, invasion biology, conservation, fisheries and aquaculture, agriculture, forestry, and toxicology. We also are striving to remain current and relevant to the scientific community at large. We recognize the importance of maximizing the visibility and accessibility of the research published in Evolutionary Applications both to increase its impact and to comply with funder mandates. To help achieve these goals, the publishers, with the support of the editorial team, have decided to convert Evolutionary Applications to an author-pays open access model. Evolutionary Applications thus becomes the first highly ranked journal to join the Wiley Open Access portfolio (http://www.wileyopenaccess.com) and will benefit from a number of advantages that confers. This open access initiative combined with our unique double-blind reviewing process and the fact that we remain the only journal specializing in applied evolutionary biology reaffirms our status among the most innovative journals in the field. Everyone will be now able to read Evolutionary Applications irrespective of specialty, host institution, or location. The journal will also be able to grow in size without the constraints of a page budget and thus keep better pace with our yearly increase in submissions. Authors will retain their own copyright as all articles will be published under a Creative Commons license. We hope that you share our excitement in this new development for Evolutionary Applications. We also take this opportunity to warmly thank all of the authors, reviewers, associate editors, and members of our advisory board for their hard work and dedication, which together has made the journal the high-quality publication it is today. We welcome any comments or suggestions from our readership, and as always, we welcome your best papers.
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 | 0.015 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.516 | 0.541 |
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".