Addressing the Effects of Station Network Geographical Inhomogeneity on Spatially Aggregated Verification Scores
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Meteorological station networks are often not homogeneously distributed across geographical verification domains, and usually unpopulated regions (such as deserts or forested regions) are less observed than densely populated regions (such as agricultural regions or cities). Therefore, spatially aggregated verification scores evaluated against station measurements are often dominated by the forecast performance in the regions with a denser observation network. In this study, we explore some solutions used in operational practices for reducing the effects of station network geographical inhomogeneity on spatially aggregated verification scores. The effects of network inhomogeneities on aggregated verification scores is first illustrated over Canada and high latitudes. Thinning the verifying observations to a less dense yet spatially homogeneous network (e.g., considering one station every 1° × 1° latitude–longitude sector) addresses the inhomogeneity issue, but not optimally, since it impoverishes the verification sample. In order to fully exploit the observation network, scores are spatially aggregated by applying a weight to each station, where the weights are inversely proportional to the network density around the station. The weights are evaluated by a Gaussian kernel: we describe a methodology and provide the optimal influence radius, evaluated for the SYNOP station network for different regions around the globe. We conclude that the Gaussian weighting provides more reliable results than thinning, and more representative results than considering the whole (inhomogeneous) station network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it