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Record W3047532055 · doi:10.20381/ruor-25031

Modelling Ship-Source Noise Impacts on Marine Mammals In Tallurutiup Imanga National Marine Conservation Area

2020· dissertation· en· W3047532055 on OpenAlexfundaboutno aff
Zuzanna Kochanowicz

Bibliographic record

VenueuO Research (University of Ottawa) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersGovernment of CanadaMarine Environmental Observation Prediction and Response Network
KeywordsMarine protected areaMarine conservationGeographyFisheryNoise (video)OceanographyEnvironmental scienceEnvironmental resource managementEcologyGeologyComputer scienceBiologyHabitat

Abstract

fetched live from OpenAlex

Climate change has influenced decreases in sea ice extent and thickness in the Arctic and facilitated a subsequent increase in marine traffic across the Canadian Arctic. Tallurutiup Imanga (TI), a unique National Marine Conservation Area (NMCA) which is home to rich wildlife and culture, is located in the heart of the Northwest Passage in the Canadian Arctic and has experienced some of the most rapid increases in vessel traffic in the region over the past two decades. Increases in ship traffic in this fragile and important region have the potential to negatively impact marine mammals that inhabit the area, which was the impetus for protected areas status in the first place. The focus of this thesis research was to examine the potential impacts of underwater noise from ships on marine mammals in the Tallurutiup Imanga NMCA. The approach taken involved: 1) examining historic spatial and temporal vessel traffic trends in the area of interest from 1990 to 2018, using the Canadian Coast Guard ship archive data for the Northern Canada Vessel Traffic Service (NORDREG) Zone, 2) conducting an in-depth analysis of recent traffic trends (2015-18) using spatially precise Automatic Identification System (AIS) vessel traffic data, 3) creating underwater noise profiles using in an acoustic model to produce received level values cumulatively for all vessels and also for all vessels within a single class, 4) identifying behavioural disturbance events as 500 metre cells where the received level was equal to 120 dB, which is the behavioural disturbance threshold for marine mammals defined National Ocean and Atmospheric Administration (NOAA), and 5) overlaying acoustic model outputs with important areas for marine mammals to understand the spatial extent of ship-source underwater noise impacts in TI. Study results revealed that vessel traffic in Tallurutiup Imanga has almost tripled over the past 29 years with bulk carriers and passenger ships travelling the most in 2018. In the most recent years of the study period there were also spikes in vessel traffic; 2018 saw nearly a doubling of bulk carrier traffic to Baffinland Iron Ore Mines Corporation’s Mary River Mine site on Baffin Island. From the years 2015 to 2018, there were certain areas where behavioural disturbance events overlapped beluga and narwhal core use areas (50 Percent Volume Contours), as well as observed wildlife areas (based on Inuit and local knowledge). Some areas like Eclipse Sound and Milne Inlet had an increased risk of behavioural disturbance events, especially with cargo vessels and passenger ships. These areas indicated a potential for negative impacts on marine mammals, and areas that have more disturbance events have a higher chance of being affected. The aim of this research was to inform our understanding of potential underwater noise risks to marine mammal, and to support ongoing environmental management and governance efforts that could be used to provide evidence-based decision making for future mitigation of the NMCA.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.294
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations1
Published2020
Admission routes2
Has abstractyes

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