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Record W1827263782 · doi:10.21236/ada442657

Graphical Analysis: Decompression Tables and Dive-Outcome Data

2004· report· en· W1827263782 on OpenAlexaboutno aff
H. D. Van Liew, E. T. Flynn

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsnot available
Fundersnot available
KeywordsOutcome (game theory)Computer scienceDecompressionMathematicsMedicineSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.386
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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