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Record W4394013538 · doi:10.26443/msurj.v19i1.203

Effects of Climate Change on Oceanic Sonar Use in the Upper European Continental Shelf

2024· article· en· W4394013538 on OpenAlexaff
Abigail Farkas

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsMcGill University
Fundersnot available
KeywordsContinental shelfOceanographySonarGeologyClimate changeContinental marginEnvironmental scienceClimatologyPhysical geographyGeographyPaleontology

Abstract

fetched live from OpenAlex

As the global effects of climate change become more known year by year, it becomes ever-more pertinent to examine the effects this may bring for every aspect of modern life we rely on. One topic of focus is that of multi-frequency sonar communication and navigational systems, which rely on well-established relationships relating to wave speed, signal intensity, and attenuation. We compiled data on oceanic temperature, acidity, and salinity in the Upper European Shelf, which includes the North Sea and Mediterranean Sea, from 2006 to 2072 using the CMIP5 future climate model in the RCP8.5 scenario. We calculate that the speed of sound in the northern European oceanic area will decrease by almost 18 m/s by 2072, with an average yearly decrease in sound speed by 0.37 m/s. The attenuation of sound through water will change year by year, calculated based on a higher-order polynomial regression dependent on the frequency of sonar used. The maximum operating ranges of active low-frequency, mid-frequency, and high-frequency sonar systems would theoretically change by +0.06%, -0.19%, and +0.71%, respectively per year, if no other factors are affected. Due to increased sound propagation, the ambient noise level of the ocean would also increase and have some counter-effect to the increased detection range however that increase in noise level was not quantitatively analyzed in this study.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.049
GPT teacher head0.321
Teacher spread0.272 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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