Research Bibliography for Students in History 450: A list of primary and secondary sources related to the first official United States weather forecast, issued on November 8, 1870
Bibliographic record
Abstract
The first official weather forecast in US history was delivered by the efforts of Increase Lapham and his colleagues at Milwaukee on November 8, 1870. At the time, Lapham was in Chicago working for what became the National Weather Service. The report concerned weather conditions on the Great Lakes as they pertained to Great Lakes shipping. This report marked the beginning of the National Weather Service and is thus significant for the history of American meteorology. This event occurred in the context of the massive post-Civil War economic boom, which led to the industrialization of the United States, a great increase in population, immigration, commerce, the specialization and professionalization of many scientific fields, the founding and funding of many American universities, and the emergence of an urban, college-trained professional class. In the same period, the various federal “homestead” acts and the agricultural bent of the new “land-grant” universities sought to transform as many Americans as possible into farmers and to apply scientific professionalism to farming. Some citizens embraced the new professionalism and science, hailing it as progress and viewing it with boundless optimism, while others resisted.\nMilwaukee began as a Native American village, called Minowaki (“Good Land” or “Good Country” in Ojibwe because of its rich soil and because the climate near the Lake Michigan offered more frost-free days for the Indigenous people to grow corn and other crops. Starting in the 1780s, Milwaukee became a fur-trade community dominated by the Métis, a people of mixed European and Indigenous heritage, and then an American settlement from the mid-1830s onward. By 1870 many Indigenous people remained in Milwaukee, many having returned despite being forcibly subjected to earlier processes of “removal” by the U.S. government. Many were allowed to remain on their lands, particularly if they were Christian and of mixed ancestry. A small free black population established itself, helped along by strong anti-slavery attitudes among Milwaukee’s elite, many of whom were Yankee-Yorkers. Farm women and other American women began to agitate for greater opportunities inside and outside the home, and Wisconsin experienced economic fluctuations as the state shifted from producing grain to producing dairy.\nThe “Storm Signal Station,” as it was called in 1870, was an attempt to use a new technology to deal with an old problem, the frequency of November Great Lakes storms. The immediate impulse for its creation was the terrible storms of November 1869, in which a record number of ships and their cargoes were lost.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| 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".