Bibliographic record
Abstract
The Marine Landscape is a concept that originated in Canada (Roff and Taylor 2000) and was recently implemented in UK and Europe in the respective frames of the UKSeaMap (Connor 2006) and Mesh projects. It aims to describe the marine environment with respect to its main geophysical features, in terms of both the seabed and water column. Marine landscape maps are not a surrogate for genuine habitat maps which are produced by incorporating biological data from samples to the physical map. They provide a more global vision of our coastal and shelf environment in terms of their main physiographic traits and hence act as a support for national/regional policy and spatial planning. While initially applied on a more global scale (resolution of one nautical mile), the concept was taken forward and applied to the French coastal approaches where digital data sets were available at higher density. The classification used for a landscape map is quite flexible. It will not be the same in a full sedimentary type of seabed or a rocky foreshore and differs in fully marine or in more continental seas such as the Baltic, for example. It needs to be adapted on a case-by-case basis to the local physiography and also to be discussed with the main stakeholders to best serve their needs. The paper discusses the methodology used to produce a seabed map, mainly relying on data cross-tabulations within a GIS, and deals with both raster and vector data. Validation against historic habitat maps is presented. Problems linked to discrepancies in data resolution are also discussed. Finally an application making use of the landscape map for conservation issues in Brittany is discussed.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".