The 2016–2017 deep-water Canada Basin Acoustic Propagation Experiment (CANAPE): A preliminary report
Bibliographic record
Abstract
The Arctic Ocean is undergoing dramatic changes in both ice cover and ocean structure. The Canada Basin Acoustic Propagation Experiment (CANAPE), which includes both deep and shallow water components, was designed to understand the effects of changing Arctic conditions on low-frequency propagation and ambient noise. The deep-water component, which is reported on here, includes a yearlong experiment in the Canada Basin during 2016–2017, preceded by a short Pilot Study during July–August 2015. During 2016-2017, a Distributed Vertical Line Array (DVLA) receiver with 60 Hydrophone Modules was moored within a six-element acoustic transceiver array with a 150-km radius. Environmental measurements on the DVLA include 28 Sea-Bird MicroCATs and upward- and downward-looking Acoustic Doppler Current Profilers (ADCPs) located below the hydrophone array. The acoustic transceivers had sources at 175-m depth and 15 Hydrophone Modules located above the sources. Environmental measurements on the transceiver moorings include ice-profiling sonars, upward-looking ADCPs on the subsurface floats, and 10 temperature sensors located below the acoustic transceivers. The one-year deployment provides measurements at least partially in open water during summer, in the marginal ice zone (MIZ) as it transitions across the array during the spring and autumn, and under complete ice cover during winter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".