MétaCan
Menu
Back to cohort

Performance Comparison of Qur’anic Search Engines

2020· article· en· W3109675915 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2020 International Conference on Computing and Information Technology (ICCIT-1441) · 2020
Typearticle
Languageen
FieldComputer Science
TopicText and Document Classification Technologies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsComputer scienceArabicThe InternetMeaning (existential)Reading (process)Search engineInformation retrievalIslamWorld Wide WebNatural language processingLinguisticsHistory

Abstract

fetched live from OpenAlex

The trend of information retrieval related to Islamic scriptures is on the rise. One of the most popular and regularly read Islamic scripture is the Digital Quran. Digital Quran is written in Arabic and involve the use of diacritics/symbols. These diacritics assist the user in reading the Quranic verses properly and interpret the meaning correctly. However, these diacritics reduce the accuracy of Quranic verse retrieval. There are numerous Quranic websites available on the internet from where users can find and retrieve any Quranic verse. This paper investigates the performance of most popular Quranic search engines with respect to the accuracy of verse retrieval based on different observations. The findings will help in developing a more efficient search engine for Digital Quran.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.042
GPT teacher head0.301
Teacher spread0.259 · 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