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Record W2053343067 · doi:10.1021/jf025577+

Sampling and Monitoring of Biogenic Emissions by Eucalyptus Leaves Using Membrane Extraction with Sorbent Interface (MESI)

2002· article· en· W2053343067 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Waterloo
FundersMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsTenaxSorbentExtraction (chemistry)MembraneEucalyptusPolydimethylsiloxaneChromatographyChemistryGas chromatographyEnvironmental scienceAdsorptionBotanyOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Membrane extraction with sorbent interface (MESI) has been applied to monitor plant fragrance volatiles emitted into indoor air. The main components of the MESI system are a membrane module and a trap, which can be connected directly to a GC or GC-MS for simultaneous multicomponent extraction and monitoring. A polydimethylsiloxane (PDMS) membrane and two different traps, PDMS and Tenax, as well as a DC current supply for trap desorption have been applied in this research. After the membrane module is placed in contact with the plant, the MESI/GC-MS provides semicontinuous characterization of volatile compounds emitted. The MESI device has been applied to monitor the biogenic volatile organic compounds released during the first 8 h after a branch was cut from a Eucalyptus dunnii tree. The study demonstrates that the MESI system is a simple and useful tool for monitoring changes in emission processes as a function of time.

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 score0.006
Threshold uncertainty score0.307

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 integrity0.0000.000
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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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