Bibliographic record
Abstract
To combat global warming, Canada has committed to reducing greenhouse gases (GHGs) 40–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation, SciAv Gaussian algorithm and the Environment and Climate Change Canada Top-down Emission Rate Retrieval Algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance, box-flight methods produce similar estimates that agree (3–25 %) within algorithm errors (4–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv algorithm may improve emission estimation. Algorithms disagreed when non-ideal conditions occurred (i.e., under non-stationary conditions). Overall, the results provide confidence in the box-flight methods, and indicate that emissions estimates are not overly sensitive to the choice of algorithm, but demonstrate that fundamental algorithm assumptions should be assessed for each flight. Using a different method, the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG), independently mapped individual plumes with emissions 5 times larger than the source SciAv sampled three days later. The range in estimates highlights the utility of increased sampling to get a more complete understanding of the temporal variability of emissions and to identify emission sources within facilities.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.050 | 0.028 |
| Insufficient payload (model declined to judge) | 0.036 | 0.030 |
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".