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Record W3202206463 · doi:10.3390/plants10102033

Down-Regulation of Photosynthetic Electron Transport and Decline in CO2 Assimilation under Low Frequencies of Pulsed Lights

2021· article· en· W3202206463 on OpenAlexaff
Marguerite Cinq-Mars, Guy Samson

Bibliographic record

VenuePlants · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsP700PhotosynthesisElectron transport chainChemistryAnalytical Chemistry (journal)ElectronPhotochemistryPhotosystem IPhotosystem IIPhysicsBiochemistry

Abstract

fetched live from OpenAlex

The decline in CO2 assimilation in leaves exposed to decreasing frequencies of pulsed light is well characterized, in contrast to the regulation of photosynthetic electron transport under these conditions. Thus, we exposed sunflower leaves to pulsed lights of different frequencies but with the same duty ratio (25%) and averaged light intensity (575 μmoles photons m−2 s−1). The rates of net photosynthesis Pn were constant from 125 to 10 Hz, and declined by 70% from 10 to 0.1 Hz. This decline coincided with (1) a marked increase in nonphotochemical quenching (NPQ), and (2) the completion after 25 ms of illumination of the first phase of P700 photooxidation, the primary electron donor of PSI. Under longer light pulses (<5 Hz), there was a slower and larger P700 photooxidation phase that could be attributed to the larger NPQ and to a resistance of electron flow on the PSI donor side indicated by 44% slower kinetics of a P700+ dark reduction. In addition, at low frequencies, the decrease in quantum yield of photochemistry was 2.3-times larger for PSII than for PSI. Globally, our results indicate that the decline in CO2 assimilation at 10 Hz and lower frequencies coincide with the formation of NPQ and a restriction of electron flows toward PSI, favoring the accumulation of harmless P700+.

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 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 score0.013
Threshold uncertainty score0.309

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)0.0000.000

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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