Determining bubble size in aquatic sediments using wideband acoustic resonance and a bubble size distribution model: testing and application in Lake Kinneret, Israel
Bibliographic record
Abstract
Abstract Free gas in natural aquatic sediments exists within discrete bubbles, which contribute to sediment destabilization and have implications for global warming. In this study, a micro-scale bubble model is used to characterize the shapes and sizes of methane bubbles in muddy aquatic sediments, governed by the mechanical properties of these muds. Building on this, a macro-scale bubble size distribution model was developed to determine the maximum equivalent spherical bubble size and cumulative gas content. An acoustic methodology, which examines the frequency-dependent reflection coefficient of sound from gassy sediments and reveals resonant behavior, was used to test the proposed model. Five acoustic measurements were conducted in Lake Kinneret, Israel, – one in 2016 and four in 2022 – at water depths ranging from 23 to 37 m. The transmitted chirp signal swelling from 300 Hz to a maximum of 15,000 Hz, was received by a nearby vertical line array consisting of up to seven hydrophones positioned near the source. Frequency analysis of the recorded signal components – including both bottom and surface reflections as well as the reverberant coda – revealed a spectral notch around 2584 Hz in 2016 and between 3027 and 4373 Hz in 2022, depending on the measurement location. These notches correspond to a maximum equivalent spherical bubble diameter of 7.95 mm in 2016 and 4.50–6.54 mm in 2022. These results are consistent with direct measurements of bubble size distributions obtained through X-ray computed tomography of frozen sediment cores collected in Lake Kinneret in 2016 by another research group, at locations matching the acoustic experiment sites.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".