MoBiDiCK: a tool for distributed computing on the Internet
Bibliographic record
Abstract
We have developed a software tool called MoBiDiCK (Modular Big Distributed Computing Kernel) that is ultimately intended for distributed computing. In this paper, we detail the design and show results using the core components of MoBiDiCK running two different clients on a local cluster. MoBiDiCK is a database-driven system that can be used to marshal a large number of processors across the Internet in order to have them collaborate on a single computation. These utilize a message-passing API and control synchronization formalism we have developed that uses the HTTP standard and Web servers. CGI programs on the volunteer processors perform the computations. The problem domains best served by MoBiDiCK are parallel computing problems that are CPU-bound (not I/O-bound) and require minimal inter-process communication. The parallel tasks that we present include the analysis of databases of 3D protein structures and Monte Carlo simulations for ab-initio protein folding.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".