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Record W4385474931 · doi:10.5281/zenodo.8207318

A SUCCINCT PROGRAMMING LANGUAGE WITH A NATIVE DATABASE COMPONENT

2017· article· en· W4385474931 on OpenAlexaff
Hanfeng Chen and Wai-Mee Ching

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComponent (thermodynamics)Computer scienceProgramming languageFirst-generation programming languageDatabaseProgramming paradigm

Abstract

fetched live from OpenAlex

ELI is a succinct interactive programming language system organized around a few simple principles. Its main data structures include arrays, lists, dictionaries and tables. In addition, it has an integrated database management component which is capable of processing a basic set of SQL statements. ELI, with a compiler, covering the array portion of the language, is also an excellent tool for coding scientific and engineering solutions. Moreover, it is productive in writing complex applications as well, such as compilers and trading systems

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0050.007
Open science0.0040.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0170.012

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 head0.040
GPT teacher head0.274
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations0
Published2017
Admission routes1
Has abstractyes

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