Measured acoustic intensity fluctuations on the Chukchi continental shelf during Canada Basin Acoustic Propagation Experiment 2017
Bibliographic record
Abstract
One of the main objectives of the Shallow Water (SW) CANAPE experiment was to gain a thorough understanding of a yearlong propagation of broadband signals from deep to shallow water with simultaneous oceanographic and acoustic measurements together along the and across the shelf break region. Using more than eleven acoustic arrays and seven oceanographic moorings in a 30 km2 region on the Chukchi shelf this task is being done by assessing both deep water sound signatures and shallow water source transmissions. In this paper with present analysis of acoustic signals from both shallow and deep water sources on the Chukchi continental shelf for a specific time period between June and August 2017 where a 20 dB intensity drop from along-the-shelf source (S2) at 150 m water depth was observed for more than few weeks. This intensity drop is strongly correlated with occurrence of a large oceanographic event spanning the top 150 m water column due to Pacific Water outflow from Bering sea and retreat of Marginal Ice Zone (MIZ). During the same period, cross-the-shelf source (S1) was not transmitting signal but the reception from the deep water acoustic transmitters also show variability that could be correlated with the basin scale water column variability and the ice coverage. [Work supported by ONR.]
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".