Distinguishing acute and chronic effects of placental dysfunction on maternal blood pressure
Bibliographic record
Abstract
Peer review, which lies at the heart of the scientific process, has a long history; the style of ('single blind') anonymous peer review now in common use was described by the Royal Society of Edinburgh in 1731, though the basic idea is far older.Despite this, widespread application of a system of anonymous expert referees did not become commonplace until later than is often believed, around the middle of the 20 th century.Expert peer review in some form is now pretty much universal in scientific journals.So is there anything new to say? Science vs non-scienceOne point that scientists sometimes need to remember is that peer review is not just important 'internally', but externally too.Properly functioning peer review is a key way to distinguish science from non-science (nonsense?).In a world where we are bombarded by apparently scientific claims -often for things that are being sold to us -it is important to have ways of telling sales talk and science apart, and peer review is one.As Sense About Science put it, peer review is an 'essential arbiter of scientific quality'(1).A fly in this ointment, of course, is that there is peer review and peer review.Journals in the top couple of dozen, or possibly more, journals in established subject categories -such as 'physiology' -maintain rigorous review processes, as we all regularly experience.But there are a lot of journals, and reviewing standards vary widely.As a recent report for the Publishing Research Consortium puts it (2): ' Because the peer review standards of different journals vary, it is widely believed [by scientists] that almost any genuine academic manuscript, however weak, can find a peer-reviewed journal to publish it if the author is persistent enough.' So, while publication in a peer review journal is some kind of quality mark, there is a blur at the edges. Austin
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".