Some statistical considerations associated with the data assimilation of precipitation observations
Bibliographic record
Abstract
Abstract Bayes's theorem is applied to the problem of analysing temperature and moisture in a volume of air given a single observation of precipitation amount, utilizing a model of non‐convective precipitation and prior estimates of the fields. Results using different statistics and shapes of probability distributions are examined. These include normal, truncated normal, and log normal distributions with special treatment of the value zero. The uncertainly of the model's formulation is considered in addition to uncertainty of observations. The posterior distribution is multi‐modal due to the model's formulation using a conditional expression. The dominant mode may be predicted as a non‐precipitating slate by the model, although the observation indicates precipitation is present. Means and modes of posterior distributions depend sensitively both on the assumed statistics and the shapes of the underlying distributions. The results suggest that the usual minimization of a cost‐function should not be used cavalierly to assimilate precipitation observations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".