Optimizing Salt Production by Estimating Brine’s Daily Height From Seawater Evaporation at Djègbadji (Benin Republic)
Bibliographic record
Abstract
Historically, salt production at Djègbadji (Benin Republic) started five to six centuries ago, traditionally relying on labor-intensive methods, consisting on leaching salty soils to obtain brine, a core component in the process. However, salt production could be streamlined by evaporating seawater in basin nearby the Atlantic Ocean. This work aims to estimate the height of seawater with a density of 1.025 (or initial brine with a density ranging from 0.4 to 1) required to obtain, after evaporation, a final brine with a density of 1.2, and to evaluate the resulting quantity of salt. During the favorable period (November to March), it would suffice to fill a basin with saltwater to a height varying between 0.61 and 35.38 mm to obtain after evaporation, by the end of the day, a brine density (of 1.2 ) which height varies between 0.521 and 30.22 mm. Solar salt production would then require an additional six days for complete water evaporation, allowing for every six days the production of between 5.26 and 305.83 kg of salt for an area of 50 m², and between 10.52 and 611.65 kg for an area of 100 m². Thus, with an area of 1 hectare (10000 m²), it would be possible to achieve a maximum annual production of 1529.125 tons, more than 1.5 times the current annual salt production in Benin. This production could be optimized by increasing the basin's surface area and applying a material with a high calorific absorption coefficient to the basin's bottom.
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 | 0.000 | 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".