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Record W2088299541 · doi:10.1109/oceans.2012.6404918

Multiple-frequency moored sonar for continuous observations of zooplankton and fish

2012· article· en· W2088299541 on OpenAlexafffund
David Lemon, Patrick Johnston, Jan Buermans, Eduardo Loos, Gary A. Borstad, Leslie Brown

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsASL Environmental Sciences (Canada)
FundersUniversity of Victoria
KeywordsSonarZooplanktonFisheryFish <Actinopterygii>OceanographyEnvironmental scienceMarine engineeringGeologyBiologyEngineering

Abstract

fetched live from OpenAlex

Moored, internally-recording acoustic instruments can acquire continuous profiles of echoes throughout the water column, thus providing a low-cost method to study the behavior and abundance of fish and zooplankton in oceans and lakes. Calibrated sonars with several frequencies allow some information about species composition and abundance to be deduced from acoustic backscatter data. The same instrument can be configured to look up from the bottom, down from the surface or horizontally from a CTD cage. In this presentation we describe an improved low power, battery-operated multi-frequency sonar capable of autonomously collecting data at high temporal and spatial resolution for periods of up to a year. The AZFP instrument (Acoustic Zooplankton and Fish Profiler) supports up to four frequencies in a single housing. The available operating frequencies are 38, 125, 200, 455 and 770 kHz. The transducers are co-located, with the same nominal beam widths of 7° or 8°, except at 38 kHz, where the beam width is 12°. The standard AZFP can be moored at depths up to 300m, and with modified transducers as deep as 600m. The recent improvements to the instrument include replacement of the signal detector with a wide dynamic range logarithmic receiver. The linearity of the receiver response has been improved, its instantaneous dynamic range has been increased to over 80 dB, and the requirement to pre-select one of four time-varying gain settings is no longer necessary, as the expanded dynamic range eliminated the need for a time-varying gain function. Additional noise reduction methods have also been implemented. The procedures used to calibrate acoustic performance of these instruments will be discussed. Typical minimum detectable volume backscatter strengths are −100dB at 20m range to −80dB at 100m range for the 125 and 200 kHz channels, and −80dB at 20m range for the 770 kHz channel. Sixteen GigaBytes (GB) of data storage is provided using a compact flash disk, which allows high temporal sampling rates (maximum 1 Hz) to be performed for shorter deployments. For longer periods, true arithmetic averaging can be done internally in both range and time to reduce the data storage space required. Low power consumption allows the instrument to collect data on four channels out to 100m range, pinging at 0.5 Hz for 150 days on a standard 200 Ampère-hours (A-Hr) battery pack. To illustrate the potential of such observations, preliminary results from several deployments in Saanich Inlet, BC are discussed. The effects of seasonal and year-to-year variations in this area are shown in a segment of a six-year time series collected by a 200 kHz sonar mounted on the VENUS cabled observatory, with the data organized as depth-time ‘cubes’ to facilitate handling of such long time series. Data from recent nearby deployments of multiple-frequency instruments (125, 200, 455 and 770 kHz) shows examples of the additional information that can be obtained from simultaneous measurements at several frequencies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.006

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.

Opus teacher head0.038
GPT teacher head0.253
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2012
Admission routes2
Has abstractyes

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