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Record W2070392350 · doi:10.1159/000192816

Computer Calculations of Exercise Dead Space: The Role of Laminar Flow, and Development of a Clinical Prediction Formula

2009· article· en· W2070392350 on OpenAlexaff
J. M. Beeckmans, R.J. Shephard

Bibliographic record

VenueRespiration · 2009
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLaminar flowDead spaceMedicineMechanicsVolume (thermodynamics)DiffusionFlow (mathematics)AnatomyThermodynamicsRespiratory systemPhysics

Abstract

fetched live from OpenAlex

The behaviour of the anatomical dead space cannot be described by passage of a ‘square’ wave front through the conducting airways, with subsequent diffusion of alveolar gas back up the bronchial tree. A computer model based on these assumptions leads to overestimation of the dead space during both rest and exercise. It is suggested that these discrepancies arise from laminar (axial) flow in the smaller airways. A simple description of the anatomical dead space is given by the equation VD = Vf+ (Ve-200)e-kt where VD is the volume of the anatomical dead space, Vf is the volume of the conducting airway to the fifteenth order bronchioles, Ve is the volume from the sixteenth order bronchioles to the second or third order alveolar ducts, 200 is an empirical correction for laminar gas flow, k is a diffusion constant (1.0 for nitrogen, 0.8 for carbon dioxide) and t is the half length of the respiratory cycle, measured in seconds.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.033
GPT teacher head0.340
Teacher spread0.307 · 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 designSimulation or modeling
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

Citations4
Published2009
Admission routes1
Has abstractyes

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