Supplemental Data for: Projecting the Future of Freshwater Aquaculture in Egypt Under Climate and Socio-Economic Scenarios
Bibliographic record
Abstract
Aquaculture is essential to Egypt’s food and nutrition security, contributing over 1.5 million tonnes of fish annually, approximately 80% of the country’s total fish production. However, the sector faces increasing risks from climate change and socio-economic transformations such as population growth, food demand, land-use dynamics, and governance shifts. Here, we adapt the GOMAP model for land-based systems to project aquaculture production potential (APP) under three Shared Socio-economic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) across five major governorates: Behera, Damietta, Kafr El-Sheikh, Ismailia and Port Said. Our analysis integrates species distribution models for key farmed fish species in Egypt, machine learning-based projections of pond water temperatures and the dietary demands of farmed species. Results indicate that under SSP1-2.6, most regions sustain or improve their APP through the 21st century, with governorates like Ismailia and Port Said maintaining 100% potential relative to the 2020s. In SSP2-4.5, however, APP becomes increasingly uneven; Behera and Kafr El-Sheikh decline by up to 79% and 74% respectively, by the 2090s, while Port Said and Ismailia retain higher resilience. Under SSP5-8.5, APP declines are most severe and widespread, especially for tilapia, with production potential falling below 50% in multiple regions by the end of the 21st century. Mullets and catfish show greater climate resilience across all scenarios. These findings highlight the urgent need for targeted adaptation strategies, including selective breeding, shading and aeration systems, and spatial reallocation to climatically stable regions. This modelling framework offers a valuable decision-support tool for ensuring sustainable and climate-resilient aquaculture development in Egypt.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.420 | 0.082 |
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".