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Record W6927390352 · doi:10.26071/656b0f87-59b7-440f

Some Health Parameters in Two Populations of Ringed Seals in Eastern Nunavut

2024· dataset· en· W6927390352 on OpenAlexaffabout

Bibliographic record

VenueOGSL repository · 2024
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Applications
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsBaseline (sea)WildlifeCitizen scienceResource (disambiguation)General partnershipEnvironmental impact statementPublic healthFur sealArctic

Abstract

fetched live from OpenAlex

The ringed seal (Pusa hispida) is a very important wildlife resource for many Inuit communities in Canada. Through funding from Fisheries and Oceans Canada’s Coastal Environmental Baseline Program and from the Nunavut Research Institute in partnership with Irving Shipbuilding Inc., information on various parameters of animal health was gathered between 2016 and 2018 in two populations of ringed seals (Eclipse Sound and Frobisher Bay) in eastern Nunavut. In collaboration with local Inuit hunters, tissue samples were collected to determine the concentrations of some essential and non-essential trace elements (including cadmium and mercury). Blood samples were also collected to determine the presence of antibodies against some pathogenic microorganisms of potential public health significance. This information will contribute to ensuring the continuous and safe use of ringed seals as a highly nutritious source of food for Inuit communities. Currently, this dataset only contains the occurrence data of ringed seals in the targeted regions as well as some measurements taken on the specimens, such as height, weight, etc. Full data will be made available when the scientific paper is published. This project is part of the Coastal Environmental Baseline Program Initiative under the Oceans Protection Plan of Fisheries and Oceans Canada.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.240
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.064
GPT teacher head0.356
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes2
Has abstractyes

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