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Record W2076489367 · doi:10.1029/2009gl040570

Impact of a decreasing sea ice cover on the vertical export of particulate organic carbon in the northern Laptev Sea, Siberian Arctic Ocean

2009· article· en· W2076489367 on OpenAlexafffund
Catherine Lalande, Simon Bélanger, Louis Fortier

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité du Québec à RimouskiUniversité Laval
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNetworks of Centres of Excellence of CanadaUniversity of ManitobaCanada Research ChairsArcticNetInternational Arctic Research Center, University of Alaska, Fairbanks
KeywordsArcticOceanographySea iceEnvironmental scienceArctic ice packSedimentGeologyTotal organic carbonParticulatesArctic geoengineeringPermafrostArctic sea ice declineClimatologyAntarctic sea iceGeomorphologyEcology

Abstract

fetched live from OpenAlex

Long‐term sediment traps were deployed from September 2005 to August 2007 in the northern Laptev Sea to assess the annual variability in vertical export of particulate organic carbon (POC). The second year of deployment coincided with the record low in Arctic summer ice extent reached in 2007 that resulted in an increase in marine primary production over the Siberian shelves. POC export fluxes increased during ice melt in 2007, leading to a ∼2‐fold increase in annual POC export relative to 2005–2006 over the continental slope of the Laptev Sea. These results suggest that the continuous decrease of sea ice extent could sustain increased POC export in the northern Laptev Sea and adjacent seas, potentially altering marine ecosystem structure in the Siberian Arctic.

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 score0.057
Threshold uncertainty score0.114

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.271
Teacher spread0.249 · 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

Citations65
Published2009
Admission routes2
Has abstractyes

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