Bibliographic record
Abstract
More and more libraries provide access to Acta Physiologica. After several years of increasing library access, there are far more than 3000 libraries worldwide that will allow you to check our contents on a regular basis. More than half of these libraries are situated within the United States. Reader numbers peak in March and October, during which we welcome more than 30 000 unique visitors to our journal pages. These numbers vary little over the year, except for an expected dip during the Northern Hemisphere's holiday season of July and August. Again it is the United States that leads in the online traffic statistics, but the lumped traffic by the European countries exceeds that of the United States. What has changed since last year's report (Persson 2013b) on where our readers come from? The trend remains stable, that is, the amount of libraries providing access to Acta Physiologica is steadily increasing, not only in the United States and Canada, but also in China and the rest of the world, where we see the most striking growth. These positive trends for less favoured regions are in part the result of our philanthropic activities. Citations, downloads and distribution of Acta Physiologica are measures that lag behind manuscript submissions, which reveal the most recent changes of authors' behaviour. Not only have manuscript submissions more than doubled during the latest 2 years, we have noted a particular surge in submissions from the Scandinavian countries and the United States. These developments reflect the current performance of Acta Physiologica, as recently highlighted (Persson 2012a,b, 2013a). In order to publish your very best work, it will probably become necessary to increase the volume of Acta Physiologica to accommodate more articles. This option has become feasible after becoming an online-only journal. Cost for printing and distribution is no longer affected by the amount of manuscripts published. We, the team of Acta Physiologica, are incredibly indebted to you for making our journal what it is today. We look forward to receiving your manuscript! None.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.053 |
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".