Toward predicting Arctic Ocean acoustic travel times using an Earth system model
Bibliographic record
Abstract
The hydroacoustic environment of a rapidly warming Arctic Ocean will be impacted by changing thermohaline structure, increased marine traffic, changes in sea ice coverage, and likely increases in microseism/storm noise. This will lead to obsolescence for today’s Arctic Ocean acoustic models just as the need for understanding and monitoring the Arctic acoustic environment becomes more critical. To make sophisticated predictions for coming conditions, we need fully-coupled Earth system models. Los Alamos National Laboratory is contributing to developing the Department of Energy’s Energy Exascale Earth System Model (E3SM), which can potentially predict the ocean and sea ice conditions necessary to drive an acoustics model in a rapidly-evolving Arctic Ocean. We present a preliminary analysis of a comparison of acoustic travel times in the Canada Basin calculated from E3SM simulations with measured travel times from the 2016–2017 Canada Basin Acoustic Propagation Experiment (CANAPE) and travel times computed from ice-tethered-profiler measurements of acoustic properties in the water column. The goals of this effort are to provide boundary ocean conditions to an acoustic model, to quantify the ocean acoustic implications of climate change, as well as to create a climate-aware atlas of global acoustic noise that could be applied, for example, in signal detection.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".