User manual for dynamic test batch processor
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.
Bibliographic record
Abstract
The analysis of podded propulsion devices generates a large volume of data. The collection of static data covering the entire operational range for a pod is impractical due to time and resource constraints. To improve the efficiency of podded propulsor performance analysis a method has been developed to utilize dynamic test data to achieve an equivalent measure of performance. It is impractical to process this model test data by hand or with conventional spreadsheet based tools. As such, software based tools have been developed in the MATLAB environment to aid the analyst in their task of producing an acceptable performance surface as a function of azimuthing angle and advance coefficient. This report describes one such tool and discusses the current implementation, revisions on previous methods, and the limitations of the software. The graphical user interface for the software is described as well as common troubleshooting methods. These methods proved successful in the analysis of the model test data from the model icebreaker Araon and its podded propulsion system.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it