Bibliographic record
Abstract
Man and nature’s response to changing shorelines with the highest tidal ranges in the world was the subject of a Winston Churchill Fellowship during 2006-08. The research compared current approaches to managing the Severn Estuary in the UK with other coastlines experiencing the highest tidal ranges in the world: the Bay of Fundy in Canada and the Penzhinskaya Guba in Far East Russia. The three estuaries share dynamic tides but extreme differences in culture, population density and resource use. With very different levels of development, the study explored the evolution of estuaries in relation to current management issues: i) Public awareness and marketing the tide for tourism; ii) Land use management in response to flood risk; iii) Opportunities for renewable energy using tidal power. People live near estuaries for resources, trade and leisure – but need to co-exist with the rich natural environment. The potential for conflict between man and nature is high around developed estuaries like the Severn Estuary. The contrast between resource utilisation around the Severn Estuary occupied by over 3 million people, compared to the Bay of Fundy with less than 1 % of the population and less than 0.1 % of the population around the remote Russian estuary, posed useful questions about sustainable resource use and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".