Welcome to the Sixth Volume of Metabolomics!
Bibliographic record
Abstract
It has been an exciting five years and last year we received our first impact factor from Thomson Reuters, calculated as 3.254. This is an excellent result and one I am delighted with. Congratulations to all our authors for sending us their most exciting and novel work, as well as top-notch reviews and tutorials. The aim is to improve this year-on-year by continuing to publish high quality, interesting and timely articles. Unfortunately for some papers this will result in rejection: our current reject rate is ca. 40%. I hope you will have noticed that the front cover of Metabolomics has undergone a transformation. It was decided at our recent Editorial Board meeting, held at last year’s highly successful Metabolomics Society 5th International Meeting in Edmonton, Canada, that the Journal cover will change to allow for the inclusion of some artwork from an article featured in that current issue; this will be scientifically driven. Our first front cover is from Oresˇič’s group (Yetukuri et al. 2010) on the functional prediction of yet to be identified lipids using supervised learning methods. Congratulations to the group for being featured on our first new front cover! You may also notice that the Journal is a little larger than normal and this reflects that the field is continuing to expand. Figure 1 shows this growth is currently a healthy exponential. The journal will aim to grow with the field and so we are currently publishing ca. 14 articles per issue this year, and will consider expanding to six issues per annum
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.610 | 0.568 |
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".