Technical Note: An Algorithm for the Detection of Steady-State Measurements of Gas Emissions From Compost
Bibliographic record
Abstract
High concentrations of carbon monoxide (CO) have been observed in the enclosed composting facility at the Edmonton Waste Management Centre in Alberta, Canada. An elevated concentration of CO in the facility is a potential health threat to workers. Research was conducted to assess the temporal and spatial variability of CO emissions from the composting bays, using Fourier Transform Infrared spectroscopy. Repeated gas measurements of CO, CO2, and CH4 were taken above and inside the compost bed using a metal gas probe. The probe was connected to the FTIR gas analyzer, which continuously collected gas concentration data. The data collected using the FTIR resulted in a continuous time series of gas measurements, where the peaks in the data signal corresponded to gas measurements taken inside the compost, and valleys represented the gas measurements taken above the compost bed. This article describes the algorithm that was devised to determine the gas concentrations at each sampling location using the MATLAB programming environment. The algorithm was able to successfully identify the sampling locations from the continuous gas measurement data, and determine the average steady-state gas concentration above and inside of the composting bays for CO, CO2, and CH4.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".