Evaluation of different Maritime rapid environmental assessment procedures with a focus on acoustic performance
Bibliographic record
Abstract
Four different Marine Rapid Environmental Assessment (MREA) procedures are compared with a focus on underwater acoustic performance. Co-located oceanographic-acoustic data were collected during the summer of 2015 in the Northwestern Mediterranean in the framework of a sea trial led by the NATO Centre for Maritime Research and Experimentation. The data were used to link MREA procedures and ocean-acoustic validation in a seamless framework. The MREA procedures consider Conductivity Temperature Depth (CTD) data, operational products from the Copernicus Marine Service, and two dynamical downscaling systems (with and without data assimilation). A portion of the oceanographic data are used for the assimilation procedure, and the remaining portion is withheld from the assimilation system for use as an independent verifying dataset. The accuracy of modelled acoustic properties is evaluated using the sound speed estimates from the different MREA methodologies as inputs to an acoustic model, and then comparing the modelled and observed acoustic arrival intensities and temporal structure. In 95% of the studied cases, the assimilative dynamical downscaling approach provides acoustic results equaling or exceeding in skill those modelled with the sound speed extracted from CTD casts. Acoustic assessment results indicate that our implementation of dynamical downscaling has skill at oceanographic scales of 4 km, about ten times larger than the ocean model horizontal resolution.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".