Bibliographic record
Abstract
The purpose of this work was to further establish the viability of the Causality Correlation Technique as a diagnostic tool for the treatment of noise problems. Acoustical dipole radiation can be generated by obstructing a subsonic flow with a rigid strut, if the strut exerts fluctuating forces on the fluid. Such forces would be the forces of reaction arising from the unsteadiness in the local flow and would form a distribution of acoustical dipole sources over the surface of the strut. For the experiments reported herein, a subsonic flow issues from a circular nozzle which is 3.8×10-2 m in diameter. The ‘quiet’ air jet operates at an exit Mach number of .217. The exit velocity is 72 m/s and is approximately uniform over the exit plane. The cylinder model is stationed at the potential core of the jet the Reynolds number is 6.3×104 (based on cylinder diameter and exit velocity). The ‘Dipole Radiation Intensity (DRI)’ is a uniquely defined and measurable quantity that is intimately related to the classical dipole. The ‘spatial distribution’ of the DRI can be constructed on a surface using the Causality Correlation Technique (see Siddon). The ‘DRI distribution’ is constructed on the surface of the rigid cylindrical strut. A diagnosis is made of the aerodynamic noise generation mechanisms using the said distribution. The far field SPL originating from the surface exclusively is predicted from the integrated DRI distribution. For laminar incident flow the predicted SPL is (69.3 ± 2.3 dB). This may be compared with an overall SPL of (70.1±.5 dB) which was directly measured.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".