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Record W2951505396 · doi:10.82308/49618

Narwhal communication and grouping behaviour: a case study in social cetacean research and monitoring

2011· article· en· W2951505396 on OpenAlexaboutno aff
Marianne Marcoux

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)ArcticGeographyBayCetaceaFisheryOceanographyEcologyBiologyGeologyArchaeology

Abstract

fetched live from OpenAlex

Narwhals (Monodon monoceros) are gregarious, toothed whales restricted to the Arctic, where habitats are changing and shipping traffic is increasing. Challenges associated with the remoteness of narwhal populations and the general difficulty of studying deep diving mammals have resulted in a lack of knowledge of narwhal social behaviour, which can only be rectified with intensive, direct and systematic observations. I studied the grouping patterns and vocal behaviour of narwhals using non-invasive methods and developed new statistical tools to analyse the data. The field work was conducted at Bruce Head, a peninsula at the mouth of Koluktoo Bay, Nunavut, during the summers of 2006-2008. Shore-based observations were used to delineate narwhal groups by sex and age class. Narwhals travelled in clusters of 1–25 individuals of mixed sex and age class. Narwhals entered the bay in larger groups than they exited. The coloration of narwhal's backs on photographs was used to estimate their age and investigate their association with individuals of similar age. To analyse these data, I developed statistical methods that examine the distribution of observations in time and their associated characteristics. Using these methods, I found that narwhals form groups with individuals of similar age. The variability and the context of usage of narwhal calls were examined from underwater recordings. Some physical characteristics of narwhal whistles seemed behaviour-specific. Both whistles and pulsed calls might serve in individual- or group-recognition. Finally, given that there is a need for sustained, local monitoring of narwhals, I explored the potential of passive acoustic methods for narwhal monitoring. An automated detector was able to correctly identify narwhal calls in a 25-day continuous recording. There was a correlation between the number of calls manually detected in non-continuous recordings and the number of narwhals observed during the recordings. Non-invasive methods can provide valuable insight into the social organization, communication and movement patterns of large numbers of non-disturbed cetaceans.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.002
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0030.002
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 head0.142
GPT teacher head0.329
Teacher spread0.188 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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