MétaCan
Menu
← Back to cohort
Record W4400218942 · doi:10.1101/2024.06.26.600872

Hornworts reveal a spatial model for pyrenoid-based CO <sub>2</sub> -concentrating mechanisms in land plants

2024· preprint· en· W4400218942 on OpenAlexaff
Tanner A. Robison, Zhen Guo Oh, Declan Lafferty, Xia Xu, Juan Carlos Villarreal, Laura H. Gunn, Fay‐Wei Li

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité Laval
FundersTriad FoundationPacific Northwest National LaboratoryNational Science Foundation
KeywordsPyrenoidComputer scienceEnvironmental scienceBiologyChloroplast

Abstract

fetched live from OpenAlex

ABSTRACT Pyrenoid-based CO 2 -concentrating mechanisms (pCCMs) turbocharge photosynthesis by saturating CO 2 around Rubisco. Hornworts are the only land plants with a pCCM. Owing to their closer relationship to crops, hornworts could offer greater translational potential compared to the green alga Chlamydomonas, the traditional model for studying pCCM. Here we report the first thorough investigation of a hornwort pCCM using the emerging model Anthoceros agrestis . The pyrenoids in A. agrestis exhibit liquid-like properties similar to Chlamydomonas, but differ by lacking starch sheaths and being enclosed by multiple thylakoids. We found that the core pCCM components in Chlamydomonas, including BST, LCIB, and CAH3, are conserved in A. agrestis and likely have similar functions based on their subcellular localizations. Therefore, the underlying chassis for concentrating CO 2 might be shared between hornworts and Chlamydomonas, and ancestral to land plants. Our study presents the first spatial model for pCCM in a land plant, paving the way for future biochemical and genetic investigations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicPlant Water Relations and Carbon Dynamics→French-language works237,207→