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Record W2020888317 · doi:10.2478/geosc-2014-0006

Encroachment of Canals of Dhaka City,Bangladesh: An Investigative Approach

2014· article· en· W2020888317 on OpenAlexaff
Asif Ishtiaque, Mallik Mahmud, Mahmudul Hasan Rafi

Bibliographic record

VenueGeoScape · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUrbanizationFlood mythFlooding (psychology)GeographyWater resource managementVulnerability (computing)Environmental planningEnvironmental scienceEcologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Dhaka City has been suffering from many environmental problems including flooding, water logging and other related problems. Urbanization, which is occurring very fast and with larger magnitude in Dhaka, is the intrinsic reason behind these problems. High rate of urbanization causes extensive urban area expansion and as a result canals, wetland and other water bodies are quickly vanishing from the landscape. This study shows the present physical condition of the canals; identifies the processes of canal encroachment; represents the consequences of canal encroachment. 13 canals of 50 were surveyed; local people were surveyed to identify the impact and processes of encroachment. According to this study, canals of Dhaka city are under serious threat of extinction and require immediate recovery actions. Canals are being encroached in various styles and this study identifies five: unauthorized land filling, illegal construction over canal, expansion of slum, solid waste dumping, taking advantage of lack of awareness of local people as well as government agencies. However, this study also discusses the grave consequences of canal encroachment: increasing flood vulnerability, wane of ground water recharge area and ground water level, collapse of natural drainage system, loss of local ecology and biodiversity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.017
GPT teacher head0.235
Teacher spread0.217 · 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.

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

Citations22
Published2014
Admission routes1
Has abstractyes

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