The enduring role of TASK balances in wind tunnel testing at the National Research Council Canada
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3894.vid The historical and continued role of TASK internal strain-gauge balances in the work of the National Research Council Canada (NRC) is presented. From the late 1950s onward, the NRC inventory of TASK balances expanded in response to the diverse and increasingly complex requirements of wind tunnel research and client testing. TASK balances have been especially integral to the measurement of aerodynamic loads at the 1.5 m TrisonicWind Tunnel, as illustrated through examples of historical and recent applications, and in the calibration equipment used to enable their continued deployment. Decades of experience at the NRC resulted in meaningful contributions to the collaborative development of recommended practices for the calibration and use of internal balances, to the benefit of the wider wind tunnel testing community. An overview of ongoing work toward the improvement of NRC balance calibration capabilities is provided.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".