Diversity in Interpretations of Probability: Implications for Weather Forecasting
Bibliographic record
Abstract
Abstract Over the last years, probability weather forecasts have become increasingly popular due in part to the development of ensemble forecast systems. Despite its widespread use in atmospheric sciences, probability forecasting remains a subtle and ambiguous way of representing the uncertainty related to a future meteorological situation. There are several schools of thought regarding the interpretation of probabilities, none of them without flaws, internal contradictions, or paradoxes. Usually, researchers tend to have personal views that are mostly based on intuition and follow a pragmatic approach. These conceptual differences may not matter when accuracy of a probabilistic forecast is measured over a long period (e.g., through the use of Brier score), which may be useful for particular objectives such as cost/benefit decision making. However, when scientists wonder about the exact meaning of the probabilistic forecast in a single case (e.g., rare and extreme event), the differences of interpretation become important. This work intends to describe this problem by first drawing attention to the more commonly accepted interpretations of probability, and then, the consequences of these assumptions are studied. Results suggest that without agreement on the interpretation, the usefulness of the probability forecast as a tool for single events—which include record-breaking events—remains unknown. An open discussion of this topic within the community would be useful to clarify the communication among researchers, with the public and with decision makers.
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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 | 0.045 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".