MétaCan
Menu
Back to cohort
Record W2334186087 · doi:10.3354/meps11144

Under-ice microbial dimethylsulfoniopropionate metabolism during the melt period in the Canadian Arctic Archipelago

2014· article· en· W2334186087 on OpenAlexafffundabout
Virginie Galindo, Maurice Levasseur, Michael Scarratt, C. J. Mundy, Michel Gosselin, RP Kiene, Margaux Gourdal, Martine Lizotte

Bibliographic record

VenueMarine Ecology Progress Series · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCégep de RimouskiUniversity of ManitobaUniversité du Québec à RimouskiFisheries and Oceans CanadaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesArcticNetUniversity of MiamiNational Science Foundation
KeywordsDimethylsulfoniopropionateArchipelagoArcticOceanographySea icePhytoplanktonEcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

This study reports on the temporal variations in algal and bacterial metabolism of dissolved dimethylsulfoniopropionate (DMSPd) in Arctic ice-covered waters in response to the release of organic matter (OM) from the sea ice and the onset of under-ice phytoplankton growth. Sampling took place between 21 May and 21 June 2012 at a station located in Resolute Passage. A snow and ice melt event was accompanied by an important release of OM and total DMSP from the bottom ice to the water column. This input of OM coincided with increases in DMSPd and DMSPd loss rate constant at the ice-water interface and, 2 days later, with increases in DMSPd and bacterial dimethylsulfide (DMS) yields from DMSPd at 0.5 m under the ice. The different microbial responses suggest that DMSPd-rich brines were released first, followed by the release of sympagic algae due to ice melt. In both cases, the changes in DMSPd metabolism resulted in an increase in gross DMS production from 0.15 to 1.9 nmol l -1 d -1 . The initiation of phytoplankton growth resulted in increases in bacterial abundance, DMSPd loss-rate constant and DMSP-sulfur assimilation. In contrast, DMS yield remained low during the onset of phytoplankton growth, indicating that bacteria used DMSP as a carbon and sulfur source. These results show that ice DMSPd can be rapidly (<1 d) and efficiently (up to 10%) converted into DMS by bacteria once released in surface water during melt events, a process that could contribute to DMS peaks measured at the ice edge.

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 score0.799
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.189
Teacher spread0.183 · 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

Citations21
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueMarine Ecology Progress SeriesSame topicArctic and Antarctic ice dynamicsFrench-language works237,207