Bibliographic record
Abstract
Two different applications of data assimilation techniques in estimating evapotranspiration at the field scale were explored in this thesis. To improve the estimation of evapotranspiration by eddy covariance for an agricultural field in Ontario, a gap-filling method was developed based on the Kalman filter data assimilation technique. Missing eddy covariance data were replaced by this method and the results compared with several other gap-filling methods from the literature. The results demonstrated that the Kalman filtering approach developed using the relationship between latent heat flux, available energy, and vapour pressure deficit provided a closer approximation of the original data and introduced smaller errors than the other methods evaluated. Evaluation of the Kalman filter approach demonstrates the efficiency of this technique in replacing data in both small and large gaps of up to several days. The second application of data assimilation techniques was to assimilate the soil moisture data collected from the same field into a land surface model with the objective to improve evapotranspiration estimates. Near-surface soil moisture was measured at the field scale with high spatial resolution and used to update the CLASS (Canadian Land Surface Scheme) 10 times during the growing season. The results showed that assimilating soil moisture data into the CLASS could improve model latent heat flux estimates by up to 14%. Assimilation of soil moisture spatial variability into CLASS resulted in greater improvement in modeled ET compared to assimilating the mean soil moisture of the sampling area. By providing two new approaches of using data assimilation techniques in land surface flux studies, this thesis showed the efficiency of these techniques to improve the estimation of evapotranspiration. Data assimilation techniques were able to obtain better estimates of the evapotranspiration than the observations or model alone by merging the available observations with the model estimates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".