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Record W4200554844 · doi:10.3390/microorganisms9122626

Use of Stress Signals of Their Attached Bacteria to Monitor Sympagic Algae Preservation in Canadian Arctic Sediments

2021· article· en· W4200554844 on OpenAlexaffabout
Rémi Amiraux, Patricia Bonin, Christopher Burot, Rontani Jean-François

Bibliographic record

VenueMicroorganisms · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversité Laval
FundersInstitut Polaire Français Paul Emile VictorCentre National d’Etudes Spatiales
KeywordsAlgaeArcticEcologyBiologySedimentPelagic zonePaleontology

Abstract

fetched live from OpenAlex

Based on the strong aggregation of sympagic (ice-associated) algae and the high mortality or inactivity of bacteria attached to them, it was previously hypothesized that sympagic algae should be significant contributors to the export of carbon to Arctic sediments. In the present work, the lipid content of 30 sediment samples collected in the Canadian Arctic was investigated to test this hypothesis. The detection of high proportions of trans vaccenic fatty acid (resulting from cis-trans isomerase (CTI) activity of bacteria under hypersaline conditions) and 10S-hydroxyhexadec-8(trans)-enoic acid (resulting from 10S-DOX bacterial detoxification activity in the presence of deleterious free palmitoleic acid) confirmed: (i) the strong contribution of sympagic material to some Arctic sediments, and (ii) the impaired physiological status of its associated bacterial communities. Unlike terrestrial material, sympagic algae that had escaped zooplanktonic grazing appeared relatively preserved from biotic degradation in Arctic sediments. The expected reduction in sea ice cover resulting from global warming should cause a shift in the relative contributions of ice-associated vs. pelagic algae to the seafloor, and thus to a strong modification of the carbon cycle.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.227
Teacher spread0.208 · 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.

Study designBench or experimental
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

Citations5
Published2021
Admission routes2
Has abstractyes

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