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Record W4200463171 · doi:10.18280/ijdne.160606

Environmental Influences on the Settlements Patterns of Communities in the Marshes of Iraq

2021· article· en· W4200463171 on OpenAlexvenueno aff
Suhail Najim, Nadia A. Alslam, Inaam A. Al-Bazzaz

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementHarmony (color)Environmental planningGeographyEnvironmental resource managementSustainabilityMarshPsychological interventionEcologyWetlandEnvironmental scienceArchaeologyPsychology

Abstract

fetched live from OpenAlex

In the field of residential community planning, one of the appropriate places to study the mutual influences between man and the environment, away from the influences, concepts and mechanisms of contemporary planning theories are isolated environments in rural areas, and the marshlands in Iraq represent one of these models. These areas still retain the planning patterns of residential communities for thousands of years. This research attempts to conduct a descriptive study of traditional settlement patterns, which relied on the capabilities of the surrounding areas to provide planning and architectural solutions based on the environmental factor. Establishing such a clear framework for these impacts can help in any future interventions or development processes in the region and ensure that any random or irregular interventions that may have occurred previously are not repeated. Which will preserve the components and sustainability of this ecosystem and maintain the harmony and integration between the elements of the architectural environment and the natural elements.

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)0.001
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 score0.039
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.248
Teacher spread0.224 · 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
Published2021
Admission routes1
Has abstractyes

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