Ultrasonic Detection of the Intravascular Free Gas Phase in Research on Diving
Bibliographic record
Abstract
Abstract The article presents a unique atypical application of the sonography technique and a methodological description of the introduction of this technique to research. The Bayesian approach applied to validation of the Doppler method for intravascular detection of the free gas phase instead of typical statistical inference has been demonstrated in the article. It describes the place of this method in the diving research work conducted in the Polish Naval Academy without any detailed analysis of the results achieved in the studies on decompression supported by ultrasonic detection of the free gas phase in venous vessels. It is a commonly held opinion that Doppler ultrasonic detection of the intravascular free gas phase is not a procedure that can be particularly useful in decompression research. The main objection is that detection of the free gas phase in venous vessels is a weak function to predict the presence of the free gas phase in tissues and arterial blood, so this method is not suitable for assessing the risk of decompression. Only a few countries disagree with this commonly held view and use this method to assess the risk of decompression in decompression studies. France has introduced detection of the free gas phase in venous vessels for diving research and then, together with Canada, improved this method, and developed it to a standard form. Based on the published results of the Canadian research, the technique was evaluated at the Naval Academy using statistical methods. The Academy accepted and adopted the results of this research and started to use this method in its own research on decompression over 25 years ago and continues to use it to great effect.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".