Graphical Analysis: Decompression Tables and Dive-Outcome Data
Bibliographic record
Abstract
We use a graphical approach to compare prescriptions for ascent given by various air decompression tables with outcomes of experimental dives compiled in the U.S. Navy' Decompression Database. For a given dive depth, we plot times at decompression stops plus time to travel from depth to the surface (TDT) on the Y-axis end bottom time on the X-axis. The analysis dramatizes the large differences among alternative decompression instructions: tables from different sources require markedly different TDTs. For the same depth/bottom-time combinations, the TDTs for USN57 (the current U.S. Navy Standard Air table) are about one-third as long as those for VVal-18 (a table developed for the U. S. Navy). Many profiles that resulted in decompression sickness (DCS) have longer TDTs then those of the USN57 table; thus, divers developed DCS despite spending more time at stops then the table requires. To a lesser extent, the same is true for the table used by the Canadian forces. A few DCS cases occurred in profiles having longer TDTs than those of the VVal-18 table or a table prepared at the University of Pennsylvania. A table developed St Duke University enables divers to avoid DCS by avoiding long bottom times. The NMRI `99 table (generated by a U.S. Navy probabilistic model, evaluated for 2.2% risk) has far longer TPTs than almost all the experimental dives that resulted in DCS cases, end in many cases the TDTs are more than twice as long as those for VVal-18.
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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 | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".