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Record W2896815161 · doi:10.1121/1.5068557

Joint observatories following a single male Cachalot during 12 weeks —The Yukusam story

2018· article· en· W2896815161 on OpenAlexaffabout
Paul Spong, Helena Symonds, Hervé Glotin, Jared R. Towers, lisa larsson, Thomas Dakin, Scott Veirs, Elizabeth Zwamborn, james pilkinton, Pascale Giraudet, Val Veirs, Jason Wood, John K. B. Ford

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie UniversityUniversity of VictoriaFisheries and Oceans Canada
Fundersnot available
KeywordsSperm whaleWhaleObservatoryFisherySpermOceanographyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

From 11 February to 31 March 2018, a lone male sperm whale visited coastal waters from the northeast to southern ends of Vancouver Island. This whale, named "Yukusam" after the Namgis First Nation word for Hanson Island, near where the whale was first observed and recorded, is the first sperm whale recorded acoustically in the area since 1984 and is the only sperm whale ever observed in coastal waters between Vancouver Island and continental North America. The Yukusam tracking story is a showcase for the potential of acoustic observatory collaborations. Tracking a single animal over such time and distance is remarkable. It obviously helped that Yukusam was the only sperm whale in the area, but still, the experience hints at the potential for using diverse independent observatories collaboratively. We then aim to see all the observatories running automated detection classification and location software and having all the data tied in to a public database for a total of almost 500 Gb of recordings, with labels. Supplemental material @ http://sabiod.org/yukusam

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)0.003
metaresearch head score (Gemma)0.003
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0020.002
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.003

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 head0.026
GPT teacher head0.232
Teacher spread0.206 · 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
Published2018
Admission routes2
Has abstractyes

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