Modelling the global soundscape: Validating a study of the COVID-19 impact using in-shore and off-shore observatory data
Bibliographic record
Abstract
This study presents a data-model comparison of the soundscape in the Northeast Pacific Ocean in 2019 and 2020. Focussing on frequencies below 400 Hz, where the soundscape is dominated by shipping, marine mammals (at selected frequencies) and large storms, a parabolic-equation model computes the propagation loss from a grid of points to a four-dimensional, high-resolution grid of receivers. The modelling data are extracted from a dynamic model of the global shipping and wind noise soundscape that utilizes AIS ship-tracking data. A comparison of the modelling results between 2019 and 2020 revealed a marked difference, reasonably due to the COVID-19 global shutdown. These results are compared to measurements from the long-term time series collected by Ocean Networks Canada’s in-shore and off-shore cabled observatories in the Salish Sea and Northeast Pacific Ocean.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".