Driver development for NRC's LongBed Comparator's interferometer system using LabView
Bibliographic record
Abstract
Interferometer Driver, design parametersUntil now general information has been given about the LongBed Comparator set-up for which the interferometer driver needs to be written.In this succeeding chapter the reader will be informed with some more explanation about what a driver is, which functions it has to fulfill and which requirements are imposed on it. What is a DriverThe interferometer and its interface card, the hardware, are used to measure carriage displacement along the line standard.The precision requirements for this NRC LongBed Comparator are in the order of 0.1 J..Lm, which asks for automation.This is usually interpreted as designing a software program that makes sure that a measurement sequence can be carried out without interference, or with as least operator interference as possible.This software program translates operator commands to a series of bits and bytes that are sent to the interface card.This interface card sends these orders given from above to the interferometer set-up, which then performs the ordered tasks.Of course information from the laser interferometer can be send back to the user as well and is then translated from interface card bits and bytes to a language the operator understands.This driver will be used on its own but also as a sub-program by a top-user, where its purpose is to read position when positioning the carriage and to make really accurate position measurements when the carriage has settled.This top-user will be called the LongBed Comparator Driver.This driver should function as a traffic police officer, telling the Interferometer driver to perform its task followed by driving the motor and meanwhile checking for the occurrence of any flaws.Before a description will be given on the software and hardware used for the Interferometer driver its functional specifications and performance requirements will be given.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.026 |
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".