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Record W2998176525 · doi:10.5539/jsd.v13n1p18

Green Worship House Competition as an Effective Tool to Evaluate Green Pyramid Rating System (GPRS)

2019· article· en· W2998176525 on OpenAlexvenueno aff
Nancy Badawy, Merhan M. Shahda

Bibliographic record

VenueJournal of Sustainable Development · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral Packet Radio ServiceCompetition (biology)Pyramid (geometry)GPRS core networkComputer scienceBusinessWorshipSet (abstract data type)TelecommunicationsPolitical scienceMathematicsEcologyLaw

Abstract

fetched live from OpenAlex

Egypt is divided into seven regional units; each region includes a number of governorates that are connected geographically and economically. When the Green Pyramid Rating System (GPRS) was proposed, and the weightings of its categories were set, the wide variation of the potential and the challenges of each region were not taken into account. Therefore, the study focused on highlighting these differences, and the main focus on Sinai region by analyzing the experience of activating The Green Pyramid Rating System (GPRS) during a competition held for this purpose. Accordingly, this paper presents an investigation into the international GBRSs to extract the specifications of a framework to improve GPRS classification, based on the experience of activating GPRS principles in Green Worship House Competition (GWHC) and the observations of participants of the competition. The study included a summary of what was suggested during participation in the competition to activate each category of Green Pyramid Rating System (GPRS), in addition to observations and problems encountered in the design of the project and activation of (GPRS) categories, then suggestions for developing the categories and weights of each category based on active participation in the Green Worship House Competition.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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 designQualitative
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

Citations4
Published2019
Admission routes1
Has abstractyes

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