Meta-grid: Un premier pas vers un framework grid basé sur les agents:In Proceedings of 2ème Conférence Francophone sur les Architectures Logicielles (CAL'08), Mars 2008, Montréal, Québec, Canada.
Bibliographic record
Abstract
Vu l'importance et l'ampleur accordée au développement des grilles de calcul (grid), il est nécessaire d'avoir une abstraction adéquate afin de les modéliser. Dans ce but, cet article proposera une plate-forme propice au bon fonctionnement d'un futur framework orienté agents développé au dessus de grids existants. Ce meta-grid, qui est une sorte de grid abstrait représentant et incarnant des grids "idéaux" existants, prendra la forme d'une couche qui sera développée pour faire face aux problèmes dus aux différences existantes entre les grids, et aux détails techniques spécifiques à chacun d'entre eux. L'idée consiste à cacher ces détails derrière un modèle uniforme que nous allons spécifier et modéliser. La création d'une telle plate-forme exemptera le programmeur d'applications grid de se soucier des problèmes spécifiques aux environnements.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".