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Record W2795476264 · doi:10.1242/jeb.169987

Polar bears in action!

2018· article· en· W2795476264 on OpenAlexaff
Sarah L. Alderman

Bibliographic record

VenueJournal of Experimental Biology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsUrsus maritimusSea icePopulationArcticGeographyArctic ice packEcologyOceanographyGeologyBiologyMeteorology

Abstract

fetched live from OpenAlex

What's in a name? For polar bears, a lot. While their common name identifies the extreme habitat they live in, their Latin name – Ursus maritimus – reminds us that unlike all other bears, these giant white carnivores rely on a marine diet. Adult seals are a perfect meal for the meat-loving bears because they are chock-full of calorie-rich blubber and polar bears are well adapted for hunting this prey, voyaging onto sea ice and lurking by air holes, ready to ambush a seal when it surfaces to breathe. Unfortunately, the sea ice is now thinner, sparser and breaks up sooner in the spring due to climate change, and the rapidly declining polar bear populations are thought to be a direct consequence of these changes to their hunting grounds. Although this explanation for population decline is certainly logical, so far the only data to support these claims are from captive animals or are extrapolated from other carnivorous species. No one actually knows how active polar bears are on sea ice, how much energy they expend navigating the ice flows and whether they actually catch enough seals to satisfy their energy needs. So, scientists from the US Geological Survey, University of California Santa Cruz and San Diego Zoo Global teamed up to learn more about what it takes to be a top Arctic predator when your world is melting away.The team captured and tranquilized nine adult female polar bears on the Alaskan coast, then weighed each bear and collected a blood sample. Next, the researchers injected each bear with modified water that was made of unique hydrogen and oxygen ions that distinguish it from regular water. The researchers planned to quantify how many of these unique ions remained in the bears at the end of their study and to use these numbers to calculate how much energy each bear spent on the sea ice. Finally, the researchers attached a video GPS collar to each bear to record the location and activities of the charismatic beasts throughout the study period before releasing them.After about 10 days, the bears were located on the sea ice and recaptured. The researchers re-weighed the bears and collected more blood, removed the collars and freed the bears before heading back to the lab to analyse their data. After reviewing the GPS and video footage and analysing the blood, the researchers discovered that polar bears move around a lot on the sea ice, burning thousands of calories as they go. In fact, the team learned that polar bears have a higher metabolic rate than previously estimated and that each bear needed to eat one fat adult seal every 10 days just to meet its basic energy needs, let alone have enough fuel left over to gain weight, reproduce or raise cubs! Yet few of the bears in their study managed to catch adult seals, or any other food for that matter, and they lost weight as a result. As sea ice continues to dwindle, catching that fatty meal will become even harder for these Arctic predators.This study is the first to bring us out onto the ice with the polar bears; to live and hunt and breathe alongside them; to understand their world and the challenges they face. The numbers are solid – the ice is not.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.379
Threshold uncertainty score0.886

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.3790.266

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 head0.033
GPT teacher head0.323
Teacher spread0.290 · 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.

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 routes1
Has abstractyes

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