Bibliographic record
Abstract
Taken by surprise, COVID-19 questions much of what we had left unquestioned. The past year certainly left its mark in publishing too, even in physiology, which to some seems far away from the COVID-19 battlefield. However, the contrary is true. Physiology gains even more importance in times of unexpected challenges to our health. Trained to rapidly grasp data and trends, physiologists combine their deep understanding of bodily functions to the benefit of us all. The download statistics of Acta Physiologica underscore the importance of physiology in these times. It is not uncommon that our articles are downloaded thousand fold. Yet, an editorial by Khedkar and Patzak1 being downloaded over seven thousand times in only weeks is more than remarkable. In an editorial, the authors can put forward theories and knowledge as expert opinions. In times of COVID, every day is essential and immediate guidance is required even if this means taking lower evidence levels into account. Often, it requires years before articles are downloaded so often. Last year provides many exceptions to this rule. In 2020, Gothie et al,2 Larsen et al,3 Lempesis et al,4 Lomo et a,l5 Miranda-Silva et al6 and Solagna et al7 published golden articles with far over 1000 downloads within the first months. Yet one editorial takes the cake. Can you imagine having your editorial downloaded over 20 000 times within less than 6 months? It is not impossible as you can see from the editorial by Steardo, Steardo Jr, Zorec and Verkhratsky.8 In their editorial, they provide cunning insight on COVID-19 and neurological disorders. The authors put forward that coronaviruses enter the CNS by intranasal inoculation using trans-synaptic pathways. Moreover, they outline why direct CNS infection in conjunction with generalized inflammation may trigger substantial neuroinflammatory responses as displayed by activation of microglia and reactive astrogliosis. Such neuroinflammation along with hypoxia could very well lead to neuropsychiatric developments and cognitive impairments seen in COVID-19 patients. Another interesting aspect is which countries download our articles most. For Acta Physiologica, the United States have a clear lead, followed by China, Great Britain, Canada and Germany. What is more, it seems that scientists read most during spring and autumn. In the midst of the first lockdown, that is, April 2020, Acta Physiologica enjoyed the most downloads ever. Can you imagine 45 000 downloads in only a month? Obviously, in 2020, Acta Physiologica was also taken by surprise. Nevertheless, the answers Acta Physiologica provided in response to COVID-19 hit the mark. The authors declare no conflict of interest.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.702 | 0.694 |
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".