Impact of Salinity on the Physical Soil Properties in the Groundnut Basin of Senegal: Case Study of Ndiaffate
Bibliographic record
Abstract
This study was conducted during the rainy season (september) to evaluate the impact of salinity on the soil physical properties. The area of study is composed of agricultural (ZC) and salt production (SA) area, none vegetalized (TV) and little vegetalized (TA) spots.Our methodological approach is based on a sampling of soils in different depth (0-20; 20-40, and 40-60 cm), the measurement of their physical properties (bulk density, infiltration test) and chemical caracteristics (pH, EC, exchangeable bases, etc.).Our results show that the measured values of electric conductivity (14 µS.cm-1 in ZC and 3290 µS.cm-1 in SA) indicate a gradient of salinity from the agricultural activities zone (ZC) towards the salt production zone (SA). The values of bulk density and infiltration, vary according to a gradient of salinity which goes decreasing from the none vegetalized spots (TV, (> 2.40 kg.m-3; 0 mm.h-1) to the little vegetalized spots (TA, (2.4 kg.m-3; 0.2 mm.h-1), the salt production area (SA, (2.32 kg.m-3; 2.4 mm.h-1) and finally to the zone of agricultural activities (ZC, (2.12 kg.m-3; 14 mm.h-1).This result establishes a relation between the gradient of salinity and the modification of the studied soil physical parameters. The practice of salt production involves an increase in the salinity of the soils.
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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".