Geoacoustic inference and the search for ground truth
Bibliographic record
Abstract
A variety of commercial, military, and scientific applications require knowledge of seabed properties. In the ocean acoustics community, the properties typically of interest are sound speed, density, and attenuation and sometimes shear speed and attenuation. Numerous approaches have been developed to estimate these by geoacoustic inference, i.e., by measuring a quantity (e.g., reflection coefficient, transmission loss, or pressure across a hydrophone array) and estimating the seabed properties from the data. Inference requires a large number of assumptions on the depth, range, and frequency dependencies of ocean and seabed properties. These assumptions are widely, and often necessarily, made with minimal supporting information. In cases where the actual physical seabed properties are of interest there is an important requirement to validate the result. Measurements on sediment cores are widely called “ground truth.“ However, these data frequently contain bias errors and exhibit rather large uncertainties, sometimes larger than those from geoacoustic inference. Here, difficulties and opportunities associated with collecting, conducting, and interpreting measurements on cores are discussed. Cores can be a useful independent measurement of sediment properties, but should not be termed as “ground truth.“ [Work supported by the Office of Naval Research and the Centre for Maritime Research and Experimentation.]
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".