Automation of State Climate Office Processes & Products: Developing Efficient Approaches for Data Dissemination
Bibliographic record
Abstract
State Climate Offices (SCO’s) in the United States are critical conduits for improving weather and climate data in local communities. Two states do not have a state-recognized SCO: Tennessee and Massachusetts. Efforts are underway at East Tennessee State University to develop the Tennessee Climate Office (TCO). Currently, climate services and products are severely lacking across Tennessee. This thesis provides an improved methodology for an existing TCO product and outlines the development of a new product using Python scripting. Daily storm reports within the monthly climate report are automated and a Weather Forecasts Hazard Index (WFHI) web application is developed. Both products utilize data from the National Oceanic and Atmospheric Administration (NOAA), with the automated daily storm reports providing substantial time savings and the WFHI providing a high resolution web application for emergency managers and others to interpret potentially hazardous forecasts for extreme temperatures, high winds, snowfall/ice accumulation, and tornado/hail events.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".