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Record W2492109542 · doi:10.5539/enrr.v6n3p44

Ecological Landscape Planning and Design Strategies for Mangrove Communities (Hara Forests) in South-Pars Special Economic Energy Zone, Asalouyeh- Iran

2016· article· en· W2492109542 on OpenAlexvenueno aff
Mohammad Reza Masnavi, Neda Amani, Ali Ahmadzadeh

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsMangroveEcotoneHabitatSustainable developmentGeographyEcologyEnvironmental resource managementEnvironmental science

Abstract

fetched live from OpenAlex

<p class="1Body">Along with the strategic role of Asalouyeh region as an industrial zone in recent decades, there have been some growing problems regarding the ecosystems of the region due to the heavy activities such as petrochemical, industrial, transportation and related development in the area. The major region of Mangrove communities in the Persian Gulf that contains some unique ecotones has established a dynamic sustainable ecosystem integrating the relationship between sea and plateau ecosystems. It is also considered as a rich habitat for the creatures in flood and Ebb conditions. Therefore, taking in to account the increasing process of ecological destruction in the existing specific protected regions- especially the Mangrove communities, it is essential to study the structural relationships between landscape elements and patches in order to balance the ecological, social relationships as well as the environmental remediation based on the preservation of ecological structures. This study tries to examine the ecological role of Mangrove communities and their habitat to introduce the threatening factors in the "South Pars Special Economic Energy Zone" and then it suggests some strategies for ecological planning and design for protecting Mangrove communities in the area i.e. storm water management, protection and development of degraded habitats and phytoremediation, to create a framework for eco-tourism and Sustainable Development in the region.<strong></strong></p>

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.071
GPT teacher head0.313
Teacher spread0.242 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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