MétaCan
Menu
Back to cohort
Record W2944003377 · doi:10.3390/atmos10050261

Ammonia Emissions Measured Using Two Different GasFinder Open-Path Lasers

2019· article· en· W2944003377 on OpenAlexafffundabout
Hambaliou Baldé, Andrew VanderZaag, Ward Smith, R. L. Desjardins

Bibliographic record

VenueAtmosphere · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsEnvironmental scienceLaserRelative humidityWind speedAmmoniaCompostAmmonium nitrateAtmospheric sciencesLidarEnvironmental engineeringMeteorologyChemistryAgronomyPhysicsOptics

Abstract

fetched live from OpenAlex

The challenges of accurately measuring in situ ammonia (NH3) losses from agricultural systems are well known. Using an open path laser coupled with a backward Lagrangian stochastic dispersion model is a promising approach for quantifying both point- and area-sources; however, this approach requires the open path laser to detect low NH3 concentrations and small concentration differences. In this study, we compared the new GasFinder3 open path laser (Boreal laser Inc., Edmonton, Canada) with the GasFinder2 sensor, the previous version. The study took place at two locations: an outdoor open-air manure compost site, and a field of wheat stubble which was fertilized with urea ammonium nitrate. Results showed the two lasers reported similar concentrations during three days of measurements at the compost site, but differed at the field site, where concentrations were close to the minimum detection limit. The GasFinder3 had a lower standard deviation under all conditions, especially with low wind speed and high relative humidity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.273
Teacher spread0.247 · 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

Citations9
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueAtmosphereSame topicOdor and Emission Control TechnologiesFrench-language works237,207