Description and evaluation of REFIST v1.0: a regional greenhouse gas flux inversion system in Canada
Bibliographic record
Abstract
Abstract. A regional greenhouse gas flux inversion system (REFIST v1.0) is described. This paper provides a comprehensive evaluation of REFIST for three provinces in Canada that include Alberta (AB), Saskatchewan (SK) and Ontario (ON). Using year 2009 fossil fuel CO2 CarbonTracker model results as the target, the synthetic data experiment analyses examined the impacts of the errors from the Bayesian optimisation method, inversion time span, prior flux distribution, region definition and the atmospheric transport model, as well as their interactions. The posterior fluxes were estimated by two different optimisation methods, the Markov chain Monte Carlo (MCMC) simulation and cost function minimization (CFM) methods. Increasing the number of sub-regions (unknowns) beyond "optimality" can produce unstable and unrealistic fluxes for some sub-regions, and does not yield significantly different flux estimates overall. The two optimisation methods can provide comparable, stable and realistic flux results when the transport model error is small (prior R2~0.8 with synthetic observations), but both methods present difficulty when the transport model error is large (prior R2~0.3). Stable and realistic sub-regional and monthly flux estimates for the western region of AB+SK can be obtained, but not for the eastern region of ON without excluding a poorly simulated station. This indicates a real observation-based inversion will likely work for the western region for tracers with similar temporal and spatial emission characteristics to fossil fuel CO2 [e.g. wintertime CH4 in Canada]. However, improvements are needed with the current inversion setup before a real inversion is performed for the eastern region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".