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Record W4409350144 · doi:10.1139/as-2024-0054

Dynamics of dissolved carbohydrates during the spring sea ice melt season in the Canadian Arctic

2025· article· en· W4409350144 on OpenAlexafffundvenueabout
Christos Panagiotopoulos, Rémi Amiraux, Kevin Crampond, Richard Sempéré

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité du Québec à Rimouski
FundersInstitut Polaire Français Paul Emile VictorCanadian Space AgencyAgence Nationale de la Recherche
KeywordsSpring (device)OceanographySea iceArcticArctic ice packThe arcticEnvironmental scienceClimatologyPhysical geographyGeologyGeographyEngineering

Abstract

fetched live from OpenAlex

In spring 2016, the temporal evolution of organic carbon and carbohydrate concentrations was monitored in and under the sea ice during the microalgal–ice algae and subsequent phytoplankton–productive periods in western Baffin Bay (Canadian Arctic). The results showed that total carbohydrates (TCHO) closely followed total organic carbon, with the highest concentrations observed in the bottom-most few centimeters of sea ice (mean 186 µM C, n = 22). Such organic matter accumulation likely comprises labile organic matter, reflecting the presence of a concentrated algal biomass, as suggested by the significant correlation between TCHO and chlorophyll a ( r = 0.625, p < 0.05). Compositionally, the bottom-most ice core was dominated by monosaccharides, which accounted for 38%–90% of TCHO, and were probably the result of an important in situ exoenzymatic activity of polysaccharides. Conversely, total dissolved carbohydrates in the underlying water did not follow dissolved organic carbon patterns and were dominated by dissolved polysaccharides, indicating that carbohydrates may have multiple sources, not solely from microalgae in either pelagic or sympagic (ice-associated) environments. Overall, our results suggest significant differences in carbohydrate dynamics between sea ice and the under-ice water, despite the strong coupling of these systems during ice melt, highlighting the complexity of the processes occurring within these ecosystems.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.245
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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.

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

Citations1
Published2025
Admission routes4
Has abstractyes

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