Modern Analysis of Precipitation Patterns Associated with the Great New England Hurricane
Bibliographic record
Abstract
The Great New England Hurricane of 1938 struck the New England coast on September 21, 1938 following four days of substantial rainfall. The heavily pre-saturated soil created conditions conducive to disaster, with the flooding and storm surge claiming over 600 lives. Deciphering the details behind the precipitation patterns of this particularly destructive hurricane using ground-data from 12-22 September 1938 was the goal of this project.\nAs with all storms before the time of radar and satellite data, the available records had to be digitized before they could be studied. The U.S. Geological Survey’s (USGS) Hurricane Flood Records for September 1938 included data from 754 stations across the U.S. Northeast and Canada; however, the data wasn’t program-readable and there was no wide-spread consistency to when the measurements were taken (daily, weekly, midday, midnight, etc.). This led to a detailed combing of the data to separate the correct measurements into their respective days through the entirety of the 10-day event.\nAfter the data were separated by day, maps were created in ArcMap 10.4 to study the USGS ground precipitation measurement-based patterns. Model runs from the Weather Research and Forecasting (WRF) model, initialized with 20th Century Reanalysis data, were used to reconstruct the upper-air patterns from September 17-22, 1938 to aid discussion of synoptic-scale phenomena that led to the destruction this hurricane and the prior rain event brought to New England.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".