Insights from Winter 2021-22 Using an Acoustic Zooplankton Fish Profiler - Ice in the Coastal Waters of the Nunatsiavut Region of Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Improved understanding of the role of the ocean in moderating climate and sustaining complex food webs is required to support ocean stewardship and ocean protection goals being pursued through Indigenous-led initiatives across Canada's coastlines. The traditional territorial waters of the Nunatsiavut in the Labrador Sea contain a rich and diverse marine ecosystem regulated by a combination of physical oceanography and the presence of seasonal sea ice cover, transported southward by wind and ocean currents. The Nunatsiavut Government operates a growing research program to understand and monitor this complex biophysical system to support effective environmental management. As part of this research program, they are now in the fifth year of monitoring over-winter ocean and sea ice conditions at an offshore site near Nain, Labrador. Measurements made at this site have included water temperature, salinity, dissolved oxygen, turbidity, currents, and ice drafts and velocities. Combining these measurements to understand the ocean's role in moderating climate and complex food webs is an important step in support of Indigenous-led research initiatives and ocean stewardship across Canada's coastlines. The Nunatsiavut Government has collaborated with ASL Environmental Sciences to further develop environmental monitoring at this site by supporting the first-ever deployment of the new ASL instrument known as the AZFP-ice. The AZFP-ice is designed to collect high temporal and spatial resolution measurements of ice draft and simultaneous biological observations from its calibrated (±1 dB) multifrequency acoustical sensors. The AZFP-ice is a calibrated, scientific, singlebeam echosounder. The AZFP-ice uses a narrow beam 417 kHz center frequency channel to obtain ice keel depth, similar to ASL's Ice Profiling Sonar (IPS). Simultaneous biological observations are realized using three separate channels, collecting calibrated backscatter measurements at 125 kHz, 200 kHz, and 769 kHz center frequencies. Building on ASL's experience with the Acoustic Zooplankton Fish Profiler (AZFP) sonar system, the AZFP-ice is designed to operate autonomously for up to 12 months at a time. The AZFP-ice also features an upgraded electronics package that allows up to 1 TB of data to be stored internally. Its endurance and its internal memory capacity allow for excellent temporal coverage and make the instrument well-suited for deployment in challenging environments. The (ex situ) factory calibration allows backscatter to be measured on an absolute scale, which facilitates downstream processing such as the so-called ‘dB differencing’ approach that is common in fisheries acoustics. This paper introduces the new AZFP-ice and showcases its capabilities as a tool for environmental monitoring. An IPS-5 was located near the AZFP-ice, allowing for a comparison of the observed ice characteristics. Preliminary analysis of the data collected during an over-winter deployment from 2021 to 2022 indicates that the AZFP-ice facilitates sea-ice characterization. Much of the over-winter period is dominated by landfast ice, and segments of ice from break-up to ice clearing are compared from the closely spaced AZFP-ice and IPS-5. During the period of landfast ice, the ice dynamics are simple as the ice responds to the thermodynamic forcing. Daylight hours are greatly reduced in the winter, but this environment does not experience 24-hour darkness, unlike high-latitude environments. While the ice may be motionless, the other AZFP-ice frequencies indicate that below the ice activity continues during this period of reduced daylight. In this paper, the AZFP-ice's acoustical observations of the under-ice biology are presented and discussed as we review examples from this deployment. The AZFP-ice is shown to provide a unique combination of measurements in a single instrument, offering researchers a physical context (i.e. ice thickness) alongside biological 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".