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Record W3110310230 · doi:10.7557/3.5100

Narwhal Abundance in the Eastern Canadian High Arctic in 2013

2020· article· en· W3110310230 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNAMMCO Scientific Publications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersNunavut Wildlife Management BoardGovernment of Nunavut
KeywordsArcticAerial surveyGeographyAbundance (ecology)Distance samplingFisheryBayStock (firearms)Abundance estimationSound (geography)Stock assessmentOceanographyPhysical geographyCartographyGeologyBiologyArchaeology

Abstract

fetched live from OpenAlex

In summer, narwhals (Monodon monoceros) migrate from Baffin Bay to northeastern Canada and northwest Greenland, where they are hunted by Inuit for subsistence. To prevent localized depletion, management of narwhals is based on summer stocks. The High Arctic Cetacean Survey (HACS), conducted in August 2013, was the first survey to estimate abundance of all 4 Canadian Baffin Bay narwhal summer stocks, as well as putative stocks in Jones Sound and Smith Sound, in the same summer. Narwhal abundance was estimated using a double-platform aerial survey. Distance sampling methods were used to estimate detection probability away from the track line. Mark-recapture methods were used to correct for the proportion of narwhals missed by visual observers on the track line (i.e., perception bias). We used a data-driven approach to identify single and duplicate sightings, using 4 covariates to compare differences in sightings made by front and rear observers based on: time of sighting, declination angle, group size, and species identity. Abundance in fjords was estimated using density surface modelling to account for their complex shape and uneven coverage. Estimates were corrected for availability bias (narwhals that are not available for detection because they are submerged when the aircraft passes overhead) using a new analysis of August dive behaviour data from narwhals equipped with satellite-linked time depth recorders. Corrected abundance estimates were 12,694 (95% CI: 6,324–25,481) for the Jones Sound stock; 16,360 (95% CI: 3,833–69,836) for the Smith Sound stock; 49,768 (95% CI: 32,945–75,182) for the Somerset Island stock; 35,043 (95% CI: 14,188–86,553) for the Admiralty Inlet stock; 10,489 (95% CI: 6,342–17,347) for the Eclipse Sound stock; and 17,555 (95% CI: 8,473–36,373) for the East Baffin Island stock. Total abundance for these 6 stocks was estimated at 141,908 (95% CI: 102,464–196,536). Sources of uncertainty arise from the high level of clustering observed, in particular in Admiralty Inlet, Eclipse Sound, and East Baffin Island, as well as the difficulty in identifying duplicate sightings between observers when large aggregations were encountered.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.031
GPT teacher head0.235
Teacher spread0.204 · 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