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Record W4393177403 · doi:10.1016/j.softx.2024.101682

ADM1jl: A Julia implementation of the Anaerobic Digestion Model 1

2024· article· en· W4393177403 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSoftwareX · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsNational Research Council CanadaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centre of Innovation
KeywordsImplementationAnaerobic digestionComputer scienceTask (project management)Digestion (alchemy)Anaerobic exerciseProgramming languageChemistryChromatographySystems engineeringEngineering

Abstract

fetched live from OpenAlex

The Anaerobic Digestion Model 1 is a system of differential equations that was developed by an International Water Association task group to describe the processes of anaerobic digestion. An implementation of the Anaerobic Digestion Model 1 (ADM1) must be as computationally fast and flexible as possible. The ADM1jl package was designed with those requirements in mind, exploiting the Julia Programming Language’s computational speed to create a programme that is both fast (between 15 and 800 times faster than other implementations tested, depending on the implementation being compared to) and user friendly.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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