Bibliographic record
Abstract
The reality of sound propagation outdoors is more complicated than simple geometrical spreading above a flat hard ground. Most common grounds, such as grass covered ground and layers of snow, are acoustically soft. This implies a complex reflection coefficient leading to a measured spectrum that is strongly influenced by the type of ground surface between source and receiver. Grounds may not be flat, leading to shadow zones or alternatively multiple reflections at the ground. Gradients of wind and temperature refract sound either upwards (upwind or in a temperature lapse) or downwards (downwind or in a temperature inversion), also leading to shadow zones or multiple reflections, respectively. Atmospheric turbulence causes fluctuations and scatters sound into acoustical shadow zones. Many of these features mutually interact and accurate predictions of sound transmission from source to receiver must somehow account for all of these phenomena simultaneously. Thus for example, ISO 9613 Part 2 in wide use today, attempts to account for all the phenomena empirically. In recent years the application of numerical techniques has led to significant advances. This plenary will review the various phenomena. Emphasis will be put on field measurements and simple physical interpretations. In a few cases, the predictions of ISO 9613 Part 2 will be compared with physical or numerical models
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.157 | 0.096 |
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".