Bibliographic record
Abstract
Sir, We thank Dr Rosival for his interesting comments. It is true that important authorities, such as those he cited, do state that a low pH in arterial blood can lead to a decreased level of consciousness. Nevertheless, one must ask: ‘What is the compelling evidence to believe that this is ‘cause-and-effect’, rather than an association’. Paraphrasing Beveridge, 1 one can never prove anything to be correct experimentally. Rather the scientific principle is that one ‘flawless’ experiment with an ‘ugly fact’ will disprove a ‘beautiful hypothesis’, even if the association is observed in many situations’. In this context, since the blood pH can fall below 7.0 during a sprint 2 while there is no obvious ‘deterioration in CNS status’, these data are not consistent with a direct linkage between a decreased level of consciousness and a very low blood pH. Our impression is that a decrease in CNS function may be seen when metabolic acidosis is accompanied by a low ECF volume with poor haemodynamics. 3 The essence of the argument is as follows. While it may seem obvious that the severity of the metabolic acidosis (indicated by how low the concentration of in plasma was) is the most important determinant of the H + load to be removed by the brain, the novel features of the bicarbonate buffer system described in reference 3 prompts us now to think otherwise.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.049 | 0.026 |
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".