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Record W1859884514 · doi:10.5539/ijc.v7n2p198

Impact of Salinity on the Physical Soil Properties in the Groundnut Basin of Senegal: Case Study of Ndiaffate

2015· article· en· W1859884514 on OpenAlexvenueno aff
Fary Diome, Alfred Tine

Bibliographic record

VenueInternational Journal of Chemistry · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityChemistrySoil salinityBulk densitySoil waterInfiltration (HVAC)Animal scienceSoil scienceEnvironmental scienceGeologyGeographyMeteorology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.318
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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