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Record W2115266410 · doi:10.1111/nph.13021

Slow carboxylation of <scp>R</scp>ubisco constrains the rate of carbon fixation during <scp>A</scp>ntarctic phytoplankton blooms

2014· article· en· W2115266410 on OpenAlexafffund
Jodi N. Young, Johanna A. L. Goldman, Sven A. Kranz, Philippe D. Tortell, François M. M. Morel

Bibliographic record

VenueNew Phytologist · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaTanzania Commission for Science and TechnologyNational Science Foundation
KeywordsCarboxylationPhytoplanktonCarbon fixationChemistryPhotosynthesisBiologyEcologyBiochemistryNutrient

Abstract

fetched live from OpenAlex

High-latitude oceans are areas of high primary production despite temperatures that are often well below the thermal optima of enzymes, including the key Calvin Cycle enzyme, Ribulose 1,5 bisphosphate carboxylase oxygenase (Rubisco). We measured carbon fixation rates, protein content and Rubisco abundance and catalytic rates during an intense diatom bloom in the Western Antarctic Peninsula (WAP) and in laboratory cultures of a psychrophilic diatom (Fragilariopsis cylindrus). At -1°C, the Rubisco turnover rate, kcat (c) , was 0.4 C s(-1) per site and the half saturation constant for CO2 was 15 μM (vs c. 3 C s(-1) per site and 50 μM at 20°C). To achieve high carboxylation rates, psychrophilic diatoms increased Rubisco abundance to c. 8% of biomass (vs c. 0.6% at 20°C), along with their total protein content, resulting in a low carbon : nitrogen ratio of c. 5. In psychrophilic diatoms, Rubisco must be almost fully active and near CO2 saturation to achieve carbon fixation rates observed in the WAP. Correspondingly, total protein concentrations were close to the highest ever measured in phytoplankton and likely near the maximum possible. We hypothesize that this high protein concentration, like that of Rubisco, is necessitated by slow enzyme rates, and that carbon fixation rates in the WAP are near a theoretical maximum.

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)0.002
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 score0.105
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.236
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

Citations122
Published2014
Admission routes2
Has abstractyes

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