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Record W2966527635 · doi:10.1038/s41467-019-11426-z

Manganese and iron deficiency in Southern Ocean Phaeocystis antarctica populations revealed through taxon-specific protein indicators

2019· article· en· W2966527635 on OpenAlexafffund
Miao Wu, J. Scott P. McCain, Elden Rowland, Rob Middag, Mats Sandgren, Andrew E. Allen, Erin M. Bertrand

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversitySvenska Forskningsrådet FormasNational Science FoundationVetenskapsrådetOffice of Polar ProgramsGordon and Betty Moore Foundation
KeywordsManganesePhytoplanktonBiologyPhotosynthesisEcologyIron deficiencyProductivityOceanographyNutrientBotanyChemistryGeology

Abstract

fetched live from OpenAlex

Iron and light are recognized as limiting factors controlling Southern Ocean phytoplankton growth. Recent field-based evidence suggests, however, that manganese availability may also play a role. Here we examine the influence of iron and manganese on protein expression and physiology in Phaeocystis antarctica, a key Antarctic primary producer. We provide taxon-specific proteomic evidence to show that in-situ Southern Ocean Phaeocystis populations regularly experience stress due to combined low manganese and iron availability. In culture, combined low iron and manganese induce large-scale changes in the Phaeocystis proteome and result in reorganization of the photosynthetic apparatus. Natural Phaeocystis populations produce protein signatures indicating late-season manganese and iron stress, consistent with concurrently observed stimulation of chlorophyll production upon additions of manganese or iron. These results implicate manganese as an important driver of Southern Ocean productivity and demonstrate the utility of peptide mass spectrometry for identifying drivers of incomplete macronutrient consumption.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.238
Teacher spread0.222 · 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

Citations89
Published2019
Admission routes2
Has abstractyes

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