MétaCan
Menu
Back to cohort
Record W3038436016 · doi:10.5539/enrr.v10n3p1

Modeling Unsaturated Zone Water Movement in the Floodplain Wetlands of the Volta Basin

2020· article· en· W3038436016 on OpenAlexvenueno aff
Benjamin Kofi Nyarko

Bibliographic record

VenueEnvironment and Natural Resources Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsHydrology (agriculture)WetlandFloodplainVadose zoneEnvironmental scienceStructural basinFlux (metallurgy)Water balanceDrainage basinSurface waterSoil waterSubsurface flowGroundwaterGeologySoil scienceGeomorphologyEcologyGeography

Abstract

fetched live from OpenAlex

The unsaturated zone of floodplain wetlands in the White Volta River basin provides an inextricable link between basin hydrology and the sustenance of plant growth. The HYDRUS-1D model was used to derive water budgets and to estimate fluxes to understand the hydrological complexities of these wetland systems. The model result indicates Spatio-temporal variation in the volume of vertical fluxes. In 2005, the estimate for average simulated flux was 0.29 cm/month in June and 1.23 cm/month in July. Consequently, the hydraulic head increased from 138.94 m to 139.30 m for the period from June to July 2005. For all sample sites, the increase in hydraulic head occurs within July and October, coinciding with high surface water fluxes. From the calculated water balance, the average monthly estimate of bottom flux was 0.01 cm/month for 2004 and 1.1 cm/month for 2005. The flow through the unsaturated zone and discharging into the subsurface water system has a high dependency on both the soil structure and the volume of water infiltrating through the surface; the highest discharge is within the period of highest water input.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and Natural Resources ResearchSame topicHydrology and Watershed Management StudiesFrench-language works237,207