Long range intensity fluctuations on the Chukchi continental shelf measured during the year-long Canada Basin Acoustic Propagation Experiment 2017
Bibliographic record
Abstract
Analysis of one-year-long broadband transmissions from five deep water sound sources recorded on shelf during the Canada Basin Acoustic Propagation shows strong azimuthal variability. Broadband chirp signals (140–325 Hz) transmitted every 4 h were received on spatially distributed receiver arrays on the Chukchi shelf from October 2016 through 2017. Here, we present spatial, temporal, and azimuthal correlations by four receiver arrays with source-receiver distances ranging from 228.6 to 524.4 km in a cross-the-shelf orientation. Temporal fluctuations in received field are related to water column variability for each source-receiver pair. Received signals from the same source to each receiver on the shelf, separated from 15.8 to 49.63 km in “along-the-shelf” direction show large intensity fluctuation. This is related to the turbulence that signals have experienced in their path. Temporal and spatial variations of temperature profiles were measured on the shelf, however, from deep water sources to the shelf break region, there is little information. Analysis are conducted for three time zones, from fully frozen seas, transition from full ice, and ice-free conditions. The parabolic equation model is used to report the propagation of field for the ice-free condition. Limitations of the acoustic modeling due to spatially under-sampled input environmental parameters are discussed. [Work supported by ONR-322 OA.]
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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) | 0.000 | 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".