Design of a Mass Driver System Concept Using Open Source Principles
Bibliographic record
Abstract
The open source software (OSS) development process has proven capable of producing high quality software products, like the Linux operating system and Apache web server, with low development costs and low risk. This paper reports on a project attempting to adapt OSS development principles to the design of a mass driver system capable of transporting construction material from Earth surface to orbit as a series of small material packages. Recent investigations have concluded that gun launch to space (GLTS) of individual, small payloads is possible. This project is evaluating the benefits of gun launching a series of small payloads that exploit the slip-stream effect to reduce atmospheric drag. The project is attempting to use only OSS or commonly available programs, such as PhpProjekt, Cmap Tools and Microsoft Office, as the tools to collaborate and develop the mass driver system. A spreadsheet has been programmed to evaluate the design parameters of a material package stream. The preliminary evaluation of the material package stream suggests the system is too productive, that is the mass transfer rate is too large; however the evaluation process is not complete. All the results and materials related to the project can be accessed at www.massdriver.com.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".