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Record W6932020888 · doi:10.5683/sp3/jwf7k2

Replication Data for: Large-scale methane controlled release study

2024· dataset· en· W6932020888 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsMethaneTransectMixing ratioWind directionLandfill gasWind speedHydrology (agriculture)Metadata

Abstract

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This publication contains data related to a large-scale methane controlled release study for the purpose of evaluating methane measurement systems for use at landfills. The work was conducted at a closed landfill in Petrolia by FluxLab between November 6th and November 14th, 2023, during 71 different release experiments. A complete report on the study is available on the study sponsor’s webpage: https://erefdn.org/product/a-controlled-release-experiment-for-investigating-methane-measurement-performance-at-landfills/ This data repository provides an archive of release rates, release locations, downwind mixing ratio measurements where available, and meteorological data from onsite and a nearby station. The data repository can be used by the measurement community to validate landfill emission simulations, or for other work. This landfill has an excellent collection system, but minor emissions are present at points totalling about 24 kg/hr, mostly from outside of the area that was used to evaluate the measurement systems. Details on background emissions and quantification can be found in the report cited above. The data is organized into two folders: 1. Measured_data which contains: • Time series of meteorological at three locations of the site (map of locations and metadata is included) • Time series of meteorological data from the closest federal Environment Climate Change Canada (ECCC) meteorological station (metadata is included) • Time series of methane mixing ratio measurements at three locations of the site (map of locations same as meteorological stations) • Time series for downwind off-site transects collected using a mobile lab, including geolocated mixing ratios of methane and carbon dioxide, wind speed, and wind direction 2. Release_data • Source flow rates for each of the 71 experiments • Source locations within the landfill site 3. Rscripts Most of the scoring for participating measurement methods were completed using spreadsheets. However, scripts for generating some of the figures in Hossain et al. (2025) are available in this data set. For mobile measurements we equipped a research vehicle with a Los Gatos Research UGGA gas analyzer (LGR_UGGA) to measure methane and carbon dioxide at 1-2 Hz. There was a mast on top of the truck that housed environmental and positional sensors, including a gas inlet at 2.5-2.7 meters from the ground, a Gill ultrasonic anemometer for measuring wind speed and direction data, a digital compass, and a high-accuracy GPS unit for the campaign. All sensor data was logged using a CR1000X Campbell Scientific data logger. We calibrated the compass daily by driving in four cardinal directions—north, south, east, and west. On a daily basis we tested analyzer's performance using compressed air cylinders of known concentration. We also performed more rigorous multi-point assessments to validate analyzer comparisons before and after the campaign, traceable gas standards. Before each daily survey, we also performed breath tests at the gas inlet to measure and account for the time lag caused by the pump, tubing, and analyzer delay between the inlet and the analyzer. To sample the complete crosswind section of the plume, we drove slowly (~15–20 km hr-1) from the ambient toward the elevated CH4 concentration and returned to the ambient. Access to public roads was typically the limiting constraint for the quantity and placement of off-site transects. Onsite meteorological measurements including windspeed, wind direction, barometric pressure, were collected and later sent to vendors. Campbell Scientific weather sensors (MetSens200 and MetSens500) were used for all onsite meteorological measurements, which were purchased new for the project and deployed with fresh factory calibrations and instantaneous measurements were logged to a Campbell Scientific CR6 datalogger. The MetSens sensors were installed at a height of 2 m. Along with each MetSens we also recorded methane mixing ratios using a small Axetris LGD TDLS sensor, which were calibrated prior to the field deployment using traceable gas standards. Since other activities were of higher priority, we did not benchmark these methane sensors on a daily basis with gases of known concentration. For the controlled releases we used natural gas with composition of 94.5% methane, 4.5% ethane, 0.09% propane, 0.4 % nitrogen, and 0.4 % carbon dioxide, was used for the study. We used ten release points spread over 8 hectares (20 acres), or roughly one-third of the total landfill footprint. Flow controllers logged total flow rate. Alicat flow controllers were used at 10 locations, purchased new and with fresh factory calibrations. Flowmeters have an uncertainty of 0.6%. Total volumetric flowrate data from flow controllers was compared with the end-of-day gas use report from the gas supply and pressure reduction system. All flow rates are reported in this archive as total volumetric rate, without factoring gas composition as we did in the report. This project was funded by Environmental Research and Education Foundation, and National Resources Canada.

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)0.012
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.001
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 head0.124
GPT teacher head0.399
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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