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Record W2321443763 · doi:10.1007/s11738-016-2113-y

Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions

2016· article· en· W2321443763 on OpenAlexaff
Hazem M. Kalaji, Anjana Jajoo, Abdallah Oukarroum, Marián Brestič, Marek Živčák, Izabela A. Samborska, Magdalena D. Cetner, Izabela Łukasik, Vasilij Goltsev, Richard J. Ladle

Bibliographic record

VenueActa Physiologiae Plantarum · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsChlorophyll fluorescencePhotosynthesisPhotosystem IIAbiotic componentChlorophyllAbiotic stressFluorescencePlant physiologyPhotosynthetic efficiencyBotanyBiologyChemistryEcologyPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Plants living under natural conditions are exposed to many adverse factors that interfere with the photosynthetic process, leading to declines in growth, development, and yield. The recent development of Chlorophyll a fluorescence (ChlF) represents a potentially valuable new approach to study the photochemical efficiency of leaves. Specifically, the analysis of fluorescence signals provides detailed information on the status and function of Photosystem II (PSII) reaction centers, light-harvesting antenna complexes, and both the donor and acceptor sides of PSII. Here, we review the results of fast ChlF analyses of photosynthetic responses to environmental stresses, and discuss the potential scientific and practical applications of this innovative methodology. The recent availability of portable devices has significantly expanded the potential utilization of ChlF techniques, especially for the purposes of crop phenotyping and monitoring.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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 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

Citations1,247
Published2016
Admission routes1
Has abstractyes

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