Global multi-scale segmentation of continental and coastal waters from the watersheds to the continental margins
Bibliographic record
Abstract
Abstract. The complex coastline of the Earth is over 400 000 km long and about 40% of the world's population lives within 100 km of the sea. Past characterizations of the global coastline were constructed either from a continental perspective through an analysis of watershed river basin properties (COSCAT: Coastal Segmentation and related CATchments) or from an oceanic perspective, through a regionalization of the proximal and distal continental margins (LME: Large Marine Ecosystems). Here, we present a global-scale coastal segmentation, composed of three consistent levels, that includes the whole aquatic continuum with its riverine, estuarine and shelf sea components. Our work delineates comprehensive ensembles which retain the most important physical characteristics of both the land and shelf areas. The proposed multi-scale segmentation results in a distribution of global exorheic watersheds, estuaries and continental shelf seas among 45 major zones (MARCATS: MARgins and CATchments Segmentation) and 149 sub-units (COSCATS). Geographic and hydrologic parameters such as the surface area, volume and fresh water residence time are calculated for each coastal unit as well as different hypsometric profiles. Our analysis provides detailed insights into the distributions of coastal and continental shelf areas and how they connect with incoming riverine fluxes. These results can be used for regional analyses and combined with various typologies for upscaling and biogeochemical budgets. In addition, the three levels segmentation can be used for application in Earth System analysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".