A systems dynamic modelling approach to assessing elements of a weather forecasting system
Bibliographic record
Abstract
The objective of a weather forecasting system is to provide information of maximum benefit to the users. One measure of those benefits is the skill of the forecasting system. Other measures of benefits can be gained through polling and socio‐economic analyses. Assuming the benefits can be quantified, the role of management is to make investments in the weather forecasting system such that the benefits are maximized. The system capacity depends on investments in observing, in telecommunication and computing systems, in research and development, and in people. In order to study a weather forecasting system from this point of view, a system dynamics simulation model has been developed. The model incorporates different factors that contribute to the quality of a forecast and recognizes that a role of management is to divide the budget into relevant activities. The factors include capital investment, meteorological research, numbers of forecasters, and the number of weather observing stations. The system dynamics modelling technique facilitates a dynamic analysis of impacts due to differential investment options. The cases of changes in allocations of funds from specific activities are analyzed. Typical fund management scenarios based on different policy options are also simulated. Illustrative case studies are shown, recognizing the limitations in the model and the available data.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".