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Record W2413703447 · doi:10.1038/srep27234

Glacial melting: an overlooked threat to Antarctic krill

2016· article· en· W2413703447 on OpenAlexaff
Verónica Fuentes, Gastón Alurralde, Bettina Meyer, Gastón E. Aguirre, Antonio Canepa, Anne‐Cathrin Wölfl, H Christian Hass, Gabriela N. Williams, Irene R Schloss

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversité du Québec à Rimouski
FundersInstituto Antártico ArgentinoTotal FoundationDeutscher Akademischer Austauschdienst
KeywordsKrillMeltwaterAntarctic krillGlacial periodOceanographyEuphausiaGlacierEnvironmental scienceIcebergClimate changeEcologySea iceBiologyGeologyPaleontology

Abstract

fetched live from OpenAlex

Strandings of marine animals are relatively common in marine systems. However, the underlying mechanisms are poorly understood. We observed mass strandings of krill in Antarctica that appeared to be linked to the presence of glacial meltwater. Climate-induced glacial meltwater leads to an increased occurrence of suspended particles in the sea, which is known to affect the physiology of aquatic organisms. Here, we study the effect of suspended inorganic particles on krill in relation to krill mortality events observed in Potter Cove, Antarctica, between 2003 and 2012. The experimental results showed that large quantities of lithogenic particles affected krill feeding, absorption capacity and performance after only 24 h of exposure. Negative effects were related to both the threshold concentrations and the size of the suspended particles. Analysis of the stomach contents of stranded krill showed large quantities of large particles ( > 10(6 )μm(3)), which were most likely mobilized by glacial meltwater. Ongoing climate-induced glacial melting may impact the coastal ecosystems of Antarctica that rely on krill.

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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations69
Published2016
Admission routes1
Has abstractyes

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