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Record W2912794093 · doi:10.5194/amt-12-1095-2019

Comparison of slant open-path flux gradient and static closed chamber techniques to measure soil N <sub>2</sub> O emissions

2019· article· en· W2912794093 on OpenAlexaff
Mei Bai, Helen Suter, Shu Kee Lam, Thomas K. Flesch, Deli Chen

Bibliographic record

VenueAtmospheric measurement techniques · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Alberta
FundersUniversity of Melbourne
KeywordsFlux (metallurgy)Nitrous oxideEnvironmental scienceAtmospheric sciencesTRACERChemistryTrace gasAnalytical Chemistry (journal)PhysicsEnvironmental chemistryNuclear physics

Abstract

fetched live from OpenAlex

Abstract. Improving direct field measurement techniques to quantify gas emissions from cropped agricultural fields is challenging. We compared nitrous oxide (N2O) emissions measured with static closed chambers to those from a newly developed aerodynamic flux gradient (FG) approach. Measurements were made at a vegetable farm following chicken manure application. The FG calculations were made with a single open-path Fourier transform infrared (OP-FTIR) spectrometer (height of 1.45 m) deployed in a slant-path configuration, sequentially aimed at retro reflectors at heights of 0.8 and 1.8 m above ground. Hourly emissions were measured with the FG technique, but once a day between 10:00 and 13:00 with chambers. We compared the concurrent emission ratios (FG∕chamber) of these two techniques and found N2O emission rates from a celery crop farm measured at midday by FG were statistically higher (1.22–1.40 times) than those from the chambers measured at the same time. Our results suggest the OP-FTIR slant-path FG configuration worked well in this study: it was sufficiently sensitive to detect the N2O gradients over our site, giving high temporal resolution N2O emissions corresponding to a large measurement footprint.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations16
Published2019
Admission routes1
Has abstractyes

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