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Record W2559438122 · doi:10.5539/jgg.v8n4p9

Morphometric Analyses of Osun Drainage Basin, Southwestern Nigeria

2016· article· en· W2559438122 on OpenAlexvenueno aff
Akinola S. Akinwumiju, M. O. Olorunfemi

Bibliographic record

VenueJournal of Geography and Geology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater and Watershed Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInfiltration (HVAC)DrainageSurface runoffDrainage basinHydrology (agriculture)Drainage densitySTREAMSStructural basinGeologyRainwater harvestingEnvironmental scienceGeomorphologyGeographyGeotechnical engineeringEcologyBiology

Abstract

fetched live from OpenAlex

This study evaluated some morphometric parameters with a view to assessing the infiltration potential of Osun Drainage Basin (ODB), Southwestern Nigeria. Input data were derived from SPOT DEM using ArcGIS 10.3 platform. ODB has an area extent of 2,208.18 km2, and is drained by 1,560 streams with total length of 2,487.7 km. The Relief Ratio (5.6) suggests that ODB is characterized by topographic high and topographic low. Thus, infiltration potential would be low as surface runoff would have less time to infiltrate before entering the drainage channels. The computed values of Drainage Texture (0.52), Stream Number (1,560), Total Stream Length (2,487.7 m) and Main Stream Length (119 m) indicate that larger percentage of annual rainwater would leave ODB as river discharge. Stream Frequency, Basin Perimeter, Length of Overland Flow and Drainage Density influence Infiltration Number across the basin. Infiltration Number increases with increasing Stream Frequency (r = 0.95) and Drainage Density (r = 0.78); and Length of Overland Flow increases with decreasing Drainage Density (r = -0.83), Stream Frequency (r = -0.51) and Infiltration Number (r = -0.45). The study concluded that basin’s infiltration potential is moderate as suggested by the mean Infiltration Number.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.238
Teacher spread0.226 · 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 designObservational
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

Citations2
Published2016
Admission routes1
Has abstractyes

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