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Record W1560682287 · doi:10.22621/cfn.v127i1.1404

Community-Based Observations of Marine Mammal Occurrences in Groswater Bay, Labrador

2013· article· en· W1560682287 on OpenAlexaffvenueabout
Keith G. Chaulk, Daniel Michelin, Melva Williams, Tony Wolfrey

Bibliographic record

VenueThe Canadian Field-Naturalist · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMarine mammalPorpoiseMinke whaleFisheryBalaenopteraBeluga WhalePhocoenaBayBelugaOtterGeographyEcologyHarbourBiologyWhaleArctic

Abstract

fetched live from OpenAlex

Experienced observers from the community of Rigolet, Newfoundland and Labrador, recorded marine mammal observations at Rigolet on the coast of Labrador from July to September 2012. During this study, a total of nine species of marine mammals were documented. A daily average of 2.6 (range 1–5) marine mammal species were observed, with Harp Seals (Pagophilus groenlandicus) and Common Minke Whales (Balaenoptera acutorostrata) being the most common. The area is thought to be regionally important for Ringed Seal (Pusa hispida) whelping; however, Ringed Seals were not observed in large numbers. Beluga Whales (Delphinapterus leucas) were also reported anecdotally, but were less common than the other species, with the possible exception of the Harbour Porpoise (Phocoena phocoena). Labrador is experiencing significant industrial growth related to hydroelectric development and offshore oil and gas exploration. These data should aid environmental assessment and environmental effects monitoring and assist researchers in understanding regional marine mammal ecology, including species diversity, seasonal occurrence, and relative abundance.

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 score0.227
Threshold uncertainty score0.456

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
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 head0.038
GPT teacher head0.237
Teacher spread0.200 · 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

Citations1
Published2013
Admission routes3
Has abstractyes

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