The global rarity of intact coastal regions
Bibliographic record
Abstract
Abstract Management of the land-sea interface is considered essential for global conservation and sustainability objectives, as coastal regions maintain natural processes that support biodiversity and the livelihood of billions of people. However, assessments of coastal regions have focused on either strictly the terrestrial or marine realm, and as a consequence, we still have a poor understanding of the overall state of Earth’s coastal regions. Here, by integrating the terrestrial human footprint and marine cumulative human impact maps, we provide a global assessment of the anthropogenic pressures affecting coastal areas. Just 15.5% of coastal areas globally can be considered having low anthropogenic pressure, mostly found in Canada, Russia, and Greenland. Conversely, 47.9% of coastal regions are heavily impacted by humanity with most countries (84.1%) having >50% of their coastal regions degraded. Nearly half (43.3%) of protected areas across coastal regions are exposed to high human pressures. In order to meet global sustainability objectives, we identify those nations that must undertake greater actions to preserve and restore coastal regions so as to ensure global sustainable development objectives can be met.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".