NRC integrated modeling: experimental validation with SPIDERS
Bibliographic record
Abstract
The NRC integrated modelling (NRCim) toolset has been developed at the NRC Herzberg Astronomy and Astrophysics Research Centre (HAA) for many years and has been used to predict complex system performance for several projects (eg. TMT primary mirror, NFIRAOS, IRIS, GPI). Although extensive software validation has been completed to ensure the validity of the NRCim results, there has not previously been an opportunity to measure the delivered performance of an instrument and complete an experimental validation of the NRCim toolset. With the recent assembly and testing of the SPIDERS instrument (Subaru Pathfinder Instrument for Detecting Exoplanets & Retrieving Spectra), our team at HAA has used the NRCim toolset to predict the performance of the SPIDERS instrument and subsequently completed directly measurements of the performance in the presence of prescribed disturbances. The measurements of the SPIDERS performance are compared with the NRCim-predicted performance providing a direct validation of the NRCim toolset.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".