Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> In West Africa, the validation of distributed models is limited by the quality and availability of point station data measured in-situ. ERA5 is a climate reanalysis produced by European Centre for Medium-range Weather Forecasts (ECMWF) and suggested to overcome this constraint. This study assessed and compared over the Benin basins at spatial and monthly time scale, the quality of ERA5 and its variant ERA5-Land (namely LAND). ERA5 relies on the single-levels version with 0.25° x 0.25° resolution while LAND is the land surface version with 0.1° x 0.1° resolution. Four variables were collected including runoff, evapotranspiration (ETR), water table depth (WTD), and soil water content (SWC). Point station data were analyzed using the correlation performance evaluators, Mean Absolute Error (m) and Relative Mean Absolute Error (r). The results showed that LAND simulates well the peaks of mean runoff. It showed the best runoff performance in terms of correlation (~0.61) compared with ERA5 (correlation ~0.49). Both reanalysis showed high correlations (generally > 0.80) for SWC, but the correlations obtained from ETR are slightly lower (ERA5~0.58 vs. ERA5-Land~0.54). Correlations were below 0.5 on both reanalyses for WTD with slight overestimation (m=4.73 m for ERA5 vs. m=3.13 m for LAND). This study does not identify any reanalysis that is better than another, both spatially and monthly scale. Nevertheless, this study indicated that the choice of reanalyses must rely on their performance and the given water cycle element. Correcting the variables of these reanalysis could also improve their performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.022 |
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; both teacher heads agree on what is shown here.
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".