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Record W2067749416 · doi:10.1145/2616498.2616565

Once you SCOOP, no need to fork

2014· article· en· W2067749416 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSCOOPComputer scienceSpawn (biology)Python (programming language)Fork (system call)Programming languageDistributed computingOperating system

Abstract

fetched live from OpenAlex

This paper presents SCOOP, a new Python framework for automatically distributing dynamic task hierarchies. A task hierarchy refers to tasks that can recursively spawn an arbitrary number of subtasks. The underlying computing infrastructure consists of a simple list of resources. The typical use case is to run the user's main program under the umbrella of the SCOOP module, where it becomes a root task that can spawn any number of subtasks through the standard "futures" API of Python, and where these subtasks may themselves spawn other subsubtasks, etc. The full task hierarchy is dynamic in the sense that it is unknown until the end of the last running task. SCOOP automatically distributes tasks amongst available resources using dynamic load balancing. A task is nothing more than a Python callable object in conjunction with its arguments. The user need not worry about message passing implementation details; all communications are implicit.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.836
Threshold uncertainty score0.482

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 science0.0010.000
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 head0.011
GPT teacher head0.249
Teacher spread0.237 · 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