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Record W7005224986

Physical Oceanographic and Acoustic Observations of the Beaufort Sea and its Subsurface Duct Sensitivity to Deterministic Ocean Features

2022· other· en· W7005224986 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2022
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceArctic ice packBeaufort seaArcticCanada BasinDrift iceSea ice thicknessFast iceAntarctic sea ice
DOInot available

Abstract

fetched live from OpenAlex

The Arctic Ocean is rapidly transforming due to climate change, and this thesis analyzes two, year-long ocean-acoustic data sets to quantify variability in the thermohaline, current and ice structure and examine the implications for acoustic propagation in the Beaufort Sea. The two field efforts are the Canada Basin AcousticPropagation Experiment (CANAPE) and the Coordinated Arctic Acoustic Thermometry Experiment (CAATEX). These results are important since acoustics offers a unique tool for the Arctic under ice communication, navigation, and remote sensing.A key acoustic feature of the Beaufort Sea is the Beaufort duct created by the Pacific winter water (PWW), which is sandwiched between the shallow Pacific summer water (PSW) and the deeper Atlantic water (AW). This is important because this duct allows for long-range transmission without lossy interactions with sea ice or surface waves. In general, we find this duct can trap from 2-6 acoustic modes in the frequency range between several tens of Hz and several hundreds of Hz. Since fluctuations can alter the number of trapped modes and affect Transmission loss, we have analyzed variability due to spice, internal waves, eddies, and near-inertial waves, and we find spicy thermohaline structure is observed to be the most significant source of variability in the top 100 m, followed by eddies and internal waves.Acoustic interaction with the ice cover is another critical factor affecting arctic acoustics, so we have analyzed high-frequency acoustic scattering statistics from the CANAPE. Five important surface scattering epochs were identified over the seasonal cycle: open water, initial ice formation, ice solidification, ice thickening, and ice melting. The most significant changes in statistics are seen during the formation, solidification, and melting. The statistical features are comparable throughout the CANAPE region, implying similar ice properties.The stability of acoustic propagation in the Beaufort duct is another aspect of this thesis. We analyzed the oceanographic measurements from the CANAPE and CAATEX, and focused on the problem of mode coupling in the Beaufort duct,induced by deterministic ocean features such as eddies and intrusions. Here we find that deterministic variability in the PSW due to spice and stronger halocline eddies can result in enhanced coupling between acoustic modes in and out of the BD.\nHere we use the mode interaction parameter (MIP), which is a non-dimensional quantity, Γmn that quantifies the mode coupling strength between mode m (a duct mode) and all the other n modes. Strong/moderate-weak coupling is determined by MIP greater-than/less-than 1. The MIP is a function of acoustic frequency and horizontal structure, which we define as the typical half-width of Gaussian perturbation (Δ) in this thesis. Our result showed that for both large and small Δ, Γmn goes to zero, and maximal coupling occurs when Δ ranges between 1.5 and 2.2 km.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.210
Teacher spread0.198 · 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 designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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