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Record W2954336022 · doi:10.1093/jxb/erz304

High-yielding rice Takanari has superior photosynthetic response to a commercial rice Koshihikari under fluctuating light

2019· article· en· W2954336022 on OpenAlexaff
Shunsuke Adachi, Yu Tanaka, Atsuko Miyagi, Makoto Kashima, Ayumi Tezuka, Yoshihiro Toya, Shunzo Kobayashi, Satoshi Ohkubo, Hiroshi Shimizu, Maki Kawai‐Yamada, Rowan F. Sage, Atsushi J. Nagano, Wataru Yamori

Bibliographic record

VenueJournal of Experimental Botany · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversity of Toronto
FundersPrecursory Research for Embryonic Science and TechnologyJapan Science and Technology Agency
KeywordsPhotosynthesisStomatal conductanceOryza sativaCultivarChlorophyll fluorescenceLight intensityElectron transport chainAgronomyBiologyBotanyPhysics

Abstract

fetched live from OpenAlex

Leaves within crop canopies experience variable light over the course of a day, which greatly affects photosynthesis and crop productivity. Little is known about the mechanisms of the photosynthetic response to fluctuating light and their genetic control. Here, we examined gas exchange, metabolite levels, and chlorophyll fluorescence during the photosynthetic induction response in an Oryza sativa indica cultivar with high yield (Takanari) and a japonica cultivar with lower yield (Koshihikari). Takanari had a faster induction response to sudden increases in light intensity than Koshihikari, as demonstrated by faster increases in net CO2 assimilation rate, stomatal conductance, and electron transport rate. In a simulated light regime that mimicked a typical summer day, the faster induction response in Takanari increased daily CO2 assimilation by 10%. The faster response of Takanari was explained in part by its maintenance of a larger pool of Calvin-Benson cycle metabolites. Together, the rapid responses of electron transport rate, metabolic flux, and stomatal conductance in Takanari contributed to the greater daily carbon gain under fluctuating light typical of natural environments.

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 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 score0.010
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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

Citations73
Published2019
Admission routes1
Has abstractyes

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