Resilience and exploitation of mussels: the convergence of history and biology leads to overexploitation of a key marine natural resource
Bibliographic record
Abstract
History, geography, and biology have converged to establish two west–east gradients along the southern coast of South Africa, one biological, one human. Historically, the Nguni-speaking peoples of the country spread from east to west along the coast, with the later European settlers moving west to east. The two peoples met in the vicinity of the Great Fish River. Under apartheid, two “homelands”, Ciskei and Transkei, were established leading to starkly different contemporary patterns of settlement and economic development. Poverty and reliance on marine natural resources, particularly mussels, are markedly higher in these former homelands. Mussel recruitment diminishes from west to east in South Africa, and the coastline of these former homelands, dominated by the oligotrophic Agulhas Current, exhibits low primary productivity and particularly low recruitment. The result is a west–east gradient of greater poverty and dependence on marine natural resources mirrored by decreased recruitment and biological resilience of mussel populations. Similarly, mussel recruitment and growth are greater on inaccessible wave-exposed shores and toward the low shore where exploitation is more difficult. The mismatch across large and small scales between where marine resources are most able to sustain exploitation and where exploitation is most intense leads to serious environmental degradation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".