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Record W4412997823 · doi:10.18280/i2m.240302

Novel Technique for Measuring Erosion in Riverbanks: Tigris River Case at Baiji City-Iraq

2025· article· en· W4412997823 on OpenAlexvenueno aff
Wesam S. Mohammed-Ali, Mohammed F. Yass, Rasul M. Khalaf

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater and Watershed Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsErosionHydrology (agriculture)BankEnvironmental scienceGeologyWater resource managementPhysical geographyGeomorphologyGeographyRemote sensingGeotechnical engineering

Abstract

fetched live from OpenAlex

Riverbank erosion is one of the important issues and challenges faced and studied by river management experts.It is one of the main risks that threatens people and structures, as it causes the loss of nearby land from the river.Erosion happens because of hydraulic forces from river water acting on the riverbank or due to the weakening and wearing away of the soil layers that make up the riverbank.A field study of riverbank erosion was carried out over five years at Baiji city along the Tigris River in Salah Al-Din governorate on the right riverbank of the river.Field measurements were conducted from 2020 to the end of 2024, with two measurements and observations carried out each year at different periods to ensure coverage of all the different water levels of the river.This allowed for long-term monitoring of the riverbank to observe the changes occurring during the study period.The discharges during the study period ranged between 420 m /sec to 2120 m /sec.The results showed that the amount of erosion on the riverbank was 62 cm, which can be considered slightly high given the current conditions in the region, and it should be taken into account that the rate may increase if favorable conditions arise for the erosion process.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.684

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 head0.030
GPT teacher head0.273
Teacher spread0.243 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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