MétaCan
Menu
Back to cohort
Record W2795640187 · doi:10.1073/pnas.1806597115

Energy-dependent quenching adjusts the excitation diffusion length to regulate photosynthetic light harvesting

2018· article· en· W2795640187 on OpenAlexfundno aff
Doran I. G. Bennett, Graham R. Fleming, Kapil Amarnath

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersFaculty of Arts and SciencesBasic Energy SciencesNational Energy Research Scientific Computing CenterCanadian Institute for Advanced ResearchHarvard UniversityU.S. Department of EnergyOffice of ScienceHoward Hughes Medical Institute
KeywordsQuenching (fluorescence)Photosystem IIExcitationPhotosynthesisDiffusionChlorophyll fluorescenceChemical physicsNon-photochemical quenchingChemistryPhotochemistryElectron transport chainYield (engineering)FluorescenceMolecular physicsBiophysicsMaterials sciencePhysicsOpticsBiologyThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

An important determinant of crop yields is the regulation of photosystem II (PSII) light harvesting by energy-dependent quenching (qE). However, the molecular details of excitation quenching have not been quantitatively connected to the fraction of excitations converted to chemical energy by PSII reaction centers (PSII yield), which determines flux to downstream metabolism. Here, we incorporate excitation dissipation by qE into a pigment-scale model of excitation transfer and trapping for a 200 × 200-nm patch of the grana membrane. We show that excitation transport can be rigorously coarse grained to a 2D random walk with an excitation diffusion length determined by the extent of quenching. We present an alternative method for analyzing pulse amplitude-modulated chlorophyll fluorescence measurements that incorporates the effects of a variable excitation diffusion length during qE activation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.020
GPT teacher head0.277
Teacher spread0.256 · 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 designBench or experimental
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

Citations50
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicPhotosynthetic Processes and MechanismsFrench-language works237,207