The 2016–2017 deep-water Canada Basin Acoustic Propagation Experiment (CANAPE): An overview
Bibliographic record
Abstract
The Arctic Ocean is undergoing dramatic changes in the ice cover and ocean structure. The 2016–2017 deep-water Canada Basin Acoustic Propagation Experiment (CANAPE) was designed to understand the effects of changing Arctic conditions on low-frequency, long-range propagation and ambient noise. Five acoustic transceivers were deployed in a pentagon with a sixth transceiver at the center, forming an ocean acoustic tomography array with a radius of 150 km in the central Beaufort Sea. The transceivers had broadband sources centered at approximately 250 Hz located at 175-m depth in the Beaufort Duct and 15 Hydrophone Modules spanning 135 m located above the sources. A Distributed Vertical Line Array receiver with 60 Hydrophone Modules spanning 540 m was embedded within the tomographic array to provide measurements of acoustic time fronts and their fluctuations. The tomographic array was largely in open water during summer, in the marginal ice zone as it transitioned across the array during the spring and autumn, and under complete ice cover during winter. The tomographic data, together with moored data from Sea-Bird MicroCATs, Acoustic Doppler Current Profilers, ice-profiling sonars, and precision temperature sensors, will help characterize the large-scale oceanographic variability throughout the year, aiding in the interpretation the acoustic data.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".