A Case Study Documenting: The UK south-east regional strategic coastal monitoring programme
Bibliographic record
Abstract
The south-east coast of England is characterised by low-lying land susceptible to both flooding and erosion as a result of rising sea levels and soft sedimentary geology. This combined with extensive coastal development, means that the management of the coastal zone is essential. Shoreline Management Plans and coastal strategy studies have highlighted the need for a more standard approach to coastal monitoring in order maximise the use of data and to provide best value. The coastline of England and Wales is subdivided into coastal cells for the purposes of shoreline management planning (Motyka and Brampton, 1993) of which the South-East Strategic Regional Coastal Monitoring Programme covers approximately 1000km within Coastal Cells 4 and 5 between Portland Bill and the Isle of Grain. The recent approach to coastal monitoring has been both ad-hoc and unsatisfactory within the southeast of England, and elsewhere in the UK; this is evident at both regional and local scales. Data collection and analysis methodologies have been inconsistent, and coordination has been poor.The South-East Strategic Regional Coastal Monitoring Programme was introduced as a means of providing a standard, repeatable and cost-effective method of monitoring the coastal environment. It provides information for development of strategic shoreline management plans, coastal defence strategies and operational management of coastal protection and flood defence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".