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Record W7011507171

Measuring the Factors Influencing Narwhal (Monodon monoceros) Presence in Eclipse Sound, Nunavut with Passive Acoustic Monitoring

2023· other· en· W7011507171 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2023
Typeother
Languageen
FieldComputer Science
TopicDiverse Interdisciplinary Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticHabitatSound (geography)Marine conservationBaseline (sea)Marine habitatsWildlifeEctothermResource (disambiguation)
DOInot available

Abstract

fetched live from OpenAlex

Marine animal behavioral responses to anthropogenic noise present concerns for resource managers in regions such as the Canadian Arctic, where communities also rely on marine wildlife for subsistence hunting. In these areas, the potential for displacement of animals due to noise disturbance has become an important factor in management and decision-making processes. Many factors play a role in the movements and behavior of marine animals, including the influence of environmental variables like light and temperature. To determine the impacts of an added anthropogenic factor, such as underwater noise from shipping, there is a need to improve understanding of natural behavior, relationships with habitat, and responses to stressors to inform conservation and resource management. Narwhals, (Monodon monoceros) may be more sensitive to disturbances than other cetacean species due to their strong site fidelity and close association with the sea ice. Understanding their relationships with habitat and responses to added stressors is necessary for effective management and conservation efforts. Sea ice conditions and the darkness of winter make it challenging to observe these species using visual approaches. Long-term Passive Acoustic Monitoring (PAM) offers an effective means to study narwhal behaviors and their responses to environmental changes locally. In this study, I utilized PAM to establish a baseline of narwhal behavior in the Eclipse Sound region of the Canadian Arctic between 2016-2021. My research was conducted in three phases: first, quantifying the seasonal acoustic presence of narwhals; second, analyzing ship traffic patterns; and third, investigating the preliminary relationships between narwhal acoustic presence and vessel traffic. By focusing on narwhal echolocation, I examined patterns of daily presence in relation to key environmental factors such as sea ice concentration, calendar year, and time of year. Narwhal echolocation clicks were detected in all years, mainly during transitional periods of sea ice melt and formation. The calendar year, day of year, and daily sea ice cover were significant factors in predicting narwhal presence. Shipping traffic patterns indicate minimal overlap between narwhal and ships in late spring and early summer, when animals are present at the floe edge in the area. Instances of ships in proximity to the recording location when narwhals were present were most common in late summer and early fall, just before and during sea ice formation. Preliminary investigation of narwhal echolocation presence with ships within a distance of 40 km suggests that acoustic detections of narwhal decrease substantially as ships approach. The study highlights the strongly seasonal migratory behavior of narwhals as they enter and exit an important summering area and sets a foundation for future studies on the impacts of anthropogenic noise on marine mammals in the Arctic.

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 score0.200
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.037
GPT teacher head0.252
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 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
Published2023
Admission routes1
Has abstractyes

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