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Urban Landscape Planning and Soil Variation in Nigeria: Lokoja as a Case Study

2012· article· en· W1953336716 on OpenAlexvenueno aff
Michael Oloyede Alabi

Bibliographic record

VenueCanadian social science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Development and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsLandscapingVegetation (pathology)Distribution (mathematics)ColonialismTree plantingGovernment (linguistics)GeographyEnvironmental scienceAgroforestryEcologyArchaeologyBiology

Abstract

fetched live from OpenAlex

The legacies of the colonial masters of a well landscaped environment have been left to rot due to negligence and increase need for urban land for anthropogenic activities. This had led to recent attempts of revival by the government through tree planting campaigns, which have not yielded desired result. Soil factor have been found to be neglected in landscaping the urban environment, this have been attributed to failure of landscaping attempts in the study area. This research attempted to find the relationship between the vegetation species distribution and soil physical properties with use of spearman’s correlation coefficient. The findings show that relationship between vegetation species distribution and soil physical properties are not significant, this may mean that there are other factors that must be considered in determining why certain species of plant thrive in certain areas than the other. Key words: Soil degradation; Colonial legacies; Landscaping; Vegetation species

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.025
GPT teacher head0.287
Teacher spread0.263 · 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 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
Published2012
Admission routes1
Has abstractyes

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