The use of moored inverted echosounders for monitoring near-surface processes
Bibliographic record
Abstract
The near-surface ocean is a difficult place to conduct measurements of physical and biological processes. High-frequency inverted echo-sounders (IES), moored a short distance below the surface, provide a simple means for sustained, high-resolution monitoring. In particular, long duration deployments are useful in capturing transient events, such as storms or solitary wave trains, and placing their occurrence within the context of longer term trends. Lessons learned from use of these moored IES systems can be applied to modern ocean observatories. A number of long duration deployments of a self-contained 200kHz IES were made in the late 1990's. Three specific examples will be discussed. Firstly, an investigation of calibrated volume scattering from near-surface bubbles in the NE Pacific Ocean showed the frequent occurrence of vertical, plume-like structures drawn downwards up to 25 m. Within the plumes, the backscatter cross-section exhibited an exponential decay with depth, with e-folding scale in the range 0.5 to 2.5 m. Secondly, imaging of near-surface internal solitary waves was performed from a mooring near the Oregon coast, supplementing measurements made with nearby temperature sensors. These measurements showed some suggestion of scattering by turbulence. Thirdly, through the use of both backscatter intensity and echo statistics, a moored IES demonstrated quantitative monitoring of migratory zooplankton and fish populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".