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Record W2064500919 · doi:10.1139/b05-077

How do marine diatoms fix 10 billion tonnes of inorganic carbon per year?

2005· article· en· W2064500919 on OpenAlexvenueno aff
Espen Granum, John A. Raven, Richard C. Leegood

Bibliographic record

VenueCanadian Journal of Botany · 2005
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsnot available
FundersNatural Environment Research Council
KeywordsPhosphoenolpyruvate carboxykinasePhosphoenolpyruvate carboxylasePhotosynthesisTotal inorganic carbonCarbon cycleCarbon fibersBotanyBiologyChemistryEcologyBiochemistryCarbon dioxideEnzymeEcosystemMaterials science

Abstract

fetched live from OpenAlex

Diatoms are responsible for at least a quarter of inorganic carbon fixed each year in the ocean. Despite very considerable research over the last 30 years, there are still a number of fundamental unresolved aspects of inorganic carbon assimilation by marine diatoms. It is not clear how the carbon-concentrating mechanism functions and whether it is based on the direct acquisition of inorganic carbon or on a C 4 pathway, or a combination of both. Although evidence for the operation of a C 4 pathway is accumulating, the role(s) of the enzyme(s) responsible for "C 3 + C 1 " inorganic carbon assimilation in the light and dark are still matters of controversy. In this review, we discuss whether diatoms possess the enzymic and structural components necessary for a C 4 -type CO 2 -concentrating mechanism. These are compared and contrasted with other C 4 systems, both single-celled and those in terrestrial plants, which are based on Kranz anatomy. New data are presented on expression of genes that might be involved in C 4 photosynthesis, including phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase.Key words: CO 2 -concentrating mechanism, C 4 photosynthesis, marine diatoms, phosphoenolpyruvate carboxylase, phosphoenolpyruvate carboxykinase.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 designNot applicable
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

Citations104
Published2005
Admission routes1
Has abstractyes

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