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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".