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Record W2922060922

A Novel Ground Based Mass-Balance Method for Methane Emission Quantification

2018· dissertation· en· W2922060922 on OpenAlexaboutno aff
William Ivan Fujs

Bibliographic record

VenueYorkSpace (York University) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTransectEnvironmental scienceFlux (metallurgy)Point sourceMethaneAtmospheric sciencesMeteorologyPhysicsGeologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Emission of CH4 from landfills in Canada are not well constrained and in Ontario constitute the largest point source emitters. The purpose of this study was to develop and test a ground based method for quantifying CH4 emissions from large sources. Emissions of CH4 were quantified from the Keele Valley Landfill (KVL) using ground based mobile mass balance approach where a mobile cavity ring down spectroscopy (CRDS) instrument captured the downwind field of CH4 mixing ratio enhancements relative to the background. The approach involves measuring the downwind field of enhancements at successively further distances from the source until the integrated CH4 enhancements converge. On multiple days in April and May 2016 multiple transects were driven upwind and at increasing distances downwind from the KVL with the CRDS in a vehicle in order to determine integral flux emission estimates. The KVL was found to be a major local source of CH4 even though CH4 collection used for electricity generation is now terminated. An average emission rate of 429 199 kg/hr of CH4 was measured in 2017 on several days, which is less than the ECCC emission inventory value of 2149 kg/hr [2015]. The source of the discrepancy is not fully understood, but may be related to the shutdown of the KVL facility. The largest source of uncertainty in our emission estimate calculation was the height of the PBL, which

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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
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

Citations0
Published2018
Admission routes1
Has abstractyes

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