'Never such weather known in these seas': Climatic Fluctuations and the Anglo-Dutch Wars of the Seventeenth Century, 1652-1674
Bibliographic record
Abstract
Abstract In the North Sea region, the so-called Little Ice Age reached a cold, stormy nadir between 1560 and 1720, with a three-decade interruption of warmer, more tranquil weather between 1629 and 1662. Newly considered ship logbooks, diaries and other documentary evidence suggest that a rise in the frequency of easterly winds accompanied the coldest phases of the Little Ice Age, and these decadal climatic trends had consequences for regional warfare. Fought between 1652 and 1674, the Anglo-Dutch wars at sea were contested in a period of transition between decade-scale climatic regimes and consequently provide useful case studies into the relationship between meteorological trends and early modern military operations. In the first war, persistent westerly winds born of a warmer climate frequently helped crews aboard larger English warships set the terms of most naval engagements. However, during the second and third wars more frequent easterlies stimulated by a cooler climate granted critical advantages to Dutch fleets that had adopted elements of English tactics and technology. Ultimately, the changing climate of the Little Ice Age must be considered alongside human agency and the political, economic or cultural influences typically examined by military historians to explain the course of early modern warfare.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".