MétaCan
Menu
Back to cohort
Record W2086648434 · doi:10.1111/mms.12029

Remote biopsy darting and marking of polar bears

2013· article· en· W2086648434 on OpenAlexaff
Anthony M. Pagano, Elizabeth Peacock, Melissa A. McKinney

Bibliographic record

VenueMarine Mammal Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
FundersU.S. Geological SurveyU.S. Fish and Wildlife Service
KeywordsAdipose tissueUrsus maritimusBiopsySubcutaneous adipose tissueTissue samplePolarSampling (signal processing)BiologyAnimal scienceAnatomyPathologyMedicineEcologyBiochemistryComputer science

Abstract

fetched live from OpenAlex

Abstract Remote biopsy darting of polar bears ( Ursus maritimus ) is less invasive and time intensive than physical capture and is therefore useful when capture is challenging or unsafe. We worked with two manufacturers to develop a combination biopsy and marking dart for use on polar bears. We had an 80% success rate of collecting a tissue sample with a single biopsy dart and collected tissue samples from 143 polar bears on land, in water, and on sea ice. Dye marks ensured that 96% of the bears were not resampled during the same sampling period, and we recovered 96% of the darts fired. Biopsy heads with 5 mm diameters collected an average of 0.12 g of fur, tissue, and subcutaneous adipose tissue, while biopsy heads with 7 mm diameters collected an average of 0.32 g. Tissue samples were 99.3% successful (142 of 143 samples) in providing a genetic and sex identification of individuals. We had a 64% success rate collecting adipose tissue and we successfully examined fatty acid signatures in all adipose samples. Adipose lipid content values were lower compared to values from immobilized or harvested polar bears, indicating that our method was not suitable for quantifying adipose lipid content.

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 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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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 teacher head, not a consensus.

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

Citations45
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueMarine Mammal ScienceSame topicMarine animal studies overviewFrench-language works237,207