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Record W2103520987 · doi:10.1139/h06-087

Cardiovascular consequences of high-performance aircraft maneuvers: implications for effective countermeasures and laboratory-based simulations

2007· review· en· W2103520987 on OpenAlexaffvenue
Jessica M. Scott, Ben T. Esch, Len Goodman, Shannon S. D. Bredin, Mark J. Haykowsky, Darren E. R. Warburton

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typereview
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaDepartment of National DefenceDefence Research and Development Canada
Fundersnot available
KeywordsGravitational forceConsciousnessAeronauticsGravitationMedicineSimulationComputer sciencePsychologyEngineeringNeurosciencePhysics

Abstract

fetched live from OpenAlex

The gravitational stress encountered by pilots of high-performance aircraft can cause dramatic shifts in blood volume and circulatory pressure, thus placing the cardiovascular system under significant stress, sometimes resulting in loss of consciousness due to cerebral under-perfusion. Since pilots experience both increased and decreased gravitational stress in high-risk environments, it is important not only to examine the cardiovascular effects of altered gravitational exposure, but also to create effective countermeasures that will increase pilot safety. In this review, we discuss the cardiovascular consequences of rapid changes in gravitational forces. We also examine the effectiveness of the countermeasures that have been developed to combat gravity-induced loss of consciousness. Finally, we examine those current laboratory-based techniques that simulate hyper-gravity and the "push-pull effect"; making it possible to investigate the cardiovascular mechanisms responsible for maintaining cerebral perfusion and consciousness.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.027
GPT teacher head0.316
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2007
Admission routes2
Has abstractyes

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