The impact of watermills on the landscape of the River Great Ouse valley between Brampton and Hemingford Grey, 1086–1350: the identification and analysis of the extensive adaptation and construction of river channels that were engineered to power a series of valuable watermills
Bibliographic record
Abstract
This study identifies how the growth and development of a series of watermills — recorded as the most valuable in England in the Domesday Survey — significantly changed the landscape of a stretch of the River Great Ouse valley in the three centuries between the Norman Conquest (1066) and the Black Death (c 1350). By utilising remote sensing (LiDAR), cartographic analysis and fieldwork, combined with analysis of documentary (especially contemporary litigation) and literary sources, we demonstrate that landscape features that, hitherto, have either been ignored or attributed to natural processes are, in fact, the result of milling activity. The study’s findings describe and explain the national pre-eminence of water milling in Huntingdonshire in the eleventh century and show how activity expanded in the post-Conquest period. The examination helps rationalise other features of the historical landscape, such as parish boundaries. The case study presented here has broader implications for the understanding of the development of multi-channel river forms, to which end we conclude by advocating a mapping methodology that designates landscape features resulting from water milling as heritage assets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".