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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".