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Record W2338799869 · doi:10.2134/ael2015.12.0014

Simultaneous Measurements of Soil CO<sub>2</sub> and CH<sub>4</sub> Fluxes Using Laser Absorption Spectroscopy

2016· article· en· W2338799869 on OpenAlexaff
Rachhpal S. Jassal, T. Andrew Black, Iain Hawthorne, Mark S. Johnson

Bibliographic record

VenueAgricultural & Environmental Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVolume (thermodynamics)Volumetric flow rateAtmosphere (unit)SpectrometerAnalytical Chemistry (journal)LaserMixing (physics)PorosityAdsorptionAbsorption (acoustics)ChemistrySpectroscopyAirflowGreenhouse gasCavity ring-down spectroscopySoil waterMaterials scienceMechanicsOpticsEnvironmental scienceThermodynamicsSoil sciencePhysicsChromatographyComposite materialGeology

Abstract

fetched live from OpenAlex

Core Ideas A method for simultaneous measurements of soil CO2 and CH4 fluxes is presented. A laser‐based cavity ring‐down spectrometer is coupled to automated chambers. A differential equation is solved for the small flow that is exhausted. The system allowed using linear fit to mixing ratio versus chamber closure time. Veff increased by 7% due to GHG adsorption and 4% due to soil porosity. We present a method of simultaneously measuring soil CO2 and CH4 fluxes using a laser‐based cavity ring‐down spectrometer (CRDS) coupled to an automated non‐steady‐state chamber system. The differential equation describing the change in the greenhouse gas (GHG) mixing ratio in the chamber headspace following lid closure is solved for the condition when a small flow rate of chamber headspace air is pulled through the CRDS by an external pump and exhausted to the atmosphere. The small flow rate allows calculation of fluxes assuming linear relationships between the GHG mixing ratios and chamber lid closure times of a few minutes. We also calibrated the chambers for effective volume (Veff) and show that adsorption of the GHGs on the walls of the chamber caused Veff to be 7% higher than the geometric volume, with the near‐surface soil porosity causing another 4% increase in Veff.

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)0.000
metaresearch head score (Gemma)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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
GenreMethods

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

Citations5
Published2016
Admission routes1
Has abstractyes

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