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Record W2731820773 · doi:10.1088/1752-7163/aa7d10

Volatile compounds in blood headspace and nasal breath

2017· article· en· W2731820773 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Breath Research · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNOSM UniversityLakehead University
FundersSaudi Arabian Cultural Bureau
KeywordsChemistryIsopreneBreath gas analysisChromatographyGas chromatography–mass spectrometryAcetoneMethanolAcetic acidVenous bloodExhalationMass spectrometryInternal medicineBiochemistryOrganic chemistryAnesthesiaMedicine

Abstract

fetched live from OpenAlex

= 0.23), were not. Furthermore, the relative concentrations of volatiles in blood and breath varied markedly between compounds, with some, such as isoprene and acetone, having similar concentrations in each, while others, such as acetic acid, ammonia and methanol, being significantly more abundant in breath, and others, such as methanal, being detectable only in breath. We also observed that breath propanol and acetic acid concentrations were higher in male compared to female participants, and that the blood headspace methanol concentration was negatively correlated to body mass index. No relationship between volatile concentrations and age was observed. Our data suggest that breath concentrations of volatiles do not necessarily give information about the same compound in the blood stream. This is likely due to the upper airway contributing compounds over and above that originating in the circulation. An investigation of the relationship between breath volatile concentrations and that in the tissue(s) of interest should therefore become a routine part of the development process of breath-based biomarkers.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.051
GPT teacher head0.358
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