Bibliographic record
Abstract
The beginning of 2012 marks the first anniversary of Skeletal Muscle [1], and an occasion to thank the scientific community for your support of this new journal. In its first year, the journal had excellent support, publishing 20 original articles and 16 reviews. We are quite grateful to the expert reviewers who gave their time to increase the quality of these papers - and are proud that all of the submissions received reviews from truly the top experts in our field, almost in every case members of our Editorial Board. In addition, in August, Skeletal Muscle began to be indexed by PubMed [2], increasing the visibility and providing the last step in the birth of the journal. Going forward in 2012 we do hope you will submit your findings to the journal. As you can imagine, Impact Factor is only achieved by first adopters who are willing to support the journal early, before a factor is established. Further, your letters to the journal and citation of journal articles will also help to increase Skeletal Muscle's visibility, which will then in turn improve its Impact Factor - which will help every author who publishes in the journal. The bottom line is that this is truly a group endeavour; Skeletal Muscle is here for the scientist interested in this dynamic tissue. Like its namesake, it can only gather strength with use and exercise - perhaps you can start by including us in your New Year's Resolution for 2012... to help grow the journal by your support. Thanks and best of luck with your research and education this year!
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.199 | 0.147 |
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".