MAKING WAR ON JUPITER PLUVIUSTHE CULTURE AND SCIENCE OF RAINMAKINGIN THE SOUTHERN GREAT PLAINS, 1870-1913
Bibliographic record
Abstract
For two weeks in August 1891, the grounds of the "C" Ranch in rural West Texas thundered with the sound of explosions, as a federal government- sponsored expeditionary force hurled hundreds of pounds of heavy ordnance against an invisible enemy. In command of this unusual operation was "General" Robert Dyrenforth, who with $9,000 of congressional funding in pocket was doing his utmost to find out whether, as a bit of folk wisdom ran, the furious tumult and aerial concussions of battle could somehow cause rain. From tiny western hamlets to the metropolises of the East, Americans were fascinated by the sensational experiments. In magazines, newspapers, and journals, some scoffed at what they saw as a fool's errand and an egregious waste of public funds, while others were equally certain of the reality of the connection and regarded the potential windfall great enough to justifY any expense. Scientists in particular were almost unanimously doubtful (and occasionally hostile), and made their views clear in the scholarly organs of their profession. In the end, the experiments failed to prove a definitive connection; indeed, as many had predicted all along, sober assessments of the data yielded little to suggest any causal link between explosions and rainfall. Yet, curiously, this was by no means the end of the theory. Over the course of two decades, a colorful cast of characters, from an eccentric self-titled "general" to a millionaire cereal magnate-cum-social engineer, typified a stubborn core of devoted believers. Each attempted to prove (or make practical use of) the theory by discharging various weapons and explosives at the sky, hoping that raindrops would come down in exchange.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".