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Record W7036452700

Characterisation of pyrenoid components in the diatom Thalassiosira pseudonana

2023· dissertation· en· W7036452700 on OpenAlexaff

Bibliographic record

VenueWhite Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsYork University
Fundersnot available
KeywordsThalassiosira pseudonanaDiatomPyrenoidAlgaeAquatic plant
DOInot available

Abstract

fetched live from OpenAlex

Eukaryotic algae are responsible for approximately one-third of global carbon fixation.To maximise photosynthetic efficiency, algae have evolved biophysical carbon concentrating mechanisms (CCMs), concentrating inorganic carbon (Ci) to saturate Ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco) with CO2.Central to the algal CCM is the pyrenoid, a dynamic, Rubisco-containing organelle located in the chloroplast.Diatoms, an algal group with CCMs, are found in freshwater and ocean environments.They are important primary producers responsible for up to 20% of global carbon fixation.Current understanding of the diatom CCM is incomplete with key pyrenoid components unknown or uncharacterized.These components include linker proteins which bind Rubisco to drive pyrenoid formation, carbonic anhydrases (CAs) which release CO2 for fixation by Rubisco and Shell proteins hypothesized to prevent CO2 loss from the pyrenoid.This study uses bioinformatic techniques to investigate linker proteins, CAs and Shell proteins, particularly focusing on the model diatom Thalassiosira pseudonana.A novel combination of structural and phylogenetic analysis gives insight into the complexities of CCM component evolution between ecologically relevant algal lineages.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.254
Teacher spread0.206 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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