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Record W4412650846 · doi:10.1016/j.isci.2025.113148

Intracellular nutrient storage during ice algal spring blooms in the Canadian high Arctic

2025· article· en· W4412650846 on OpenAlexafffundabout
C. J. Mundy, Eva Leu, Karley Campbell, Virginie Galindo, Maurice Levasseur, Michel Poulin, Jean‐Éric Tremblay, Michel Gosselin

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité du Québec à RimouskiCanadian Museum of NatureUniversité LavalMinistère des Ressources naturelles et des Forêts
FundersNatural Resources CanadaEuropean CommissionNatural Sciences and Engineering Research Council of CanadaCanadian Museum of NatureUniversity of Manitoba
KeywordsSpring (device)ArcticNutrientOceanographyAlgal bloomThe arcticEnvironmental scienceChemistryEcologyPhytoplanktonBiologyGeologyPhysics

Abstract

fetched live from OpenAlex

Nutrient availability influences maximum biomass, speciation, cellular composition, and overall phenology of Arctic spring ice algal blooms. However, how ice algae obtain nutrients from their environment is not well understood. Previously documented positive relationships between sea ice nutrient concentrations and algal biomass implied that ice algae maintain an intracellular nutrient pool. Here, we provide direct evidence that sea ice diatoms store intracellular nitrate + nitrite and silicic acid well above that available in their ambient environment. Differential retention of intracellular pools released during standard melt processing techniques led to an increase in the apparent dissolved N:Si ratio measured in ice melt samples that likely influenced interpretations of Si-limitation in some previous studies. It is hypothesized that the ability of ice algae to store intracellular nutrient reserves represents a beneficial adaptation for ice algae to extend blooms under a periodic tidal-pulsed flux of nutrients to the ice bottom environment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.479

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 studies0.0010.000
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 head0.005
GPT teacher head0.185
Teacher spread0.179 · 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

Citations4
Published2025
Admission routes3
Has abstractyes

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