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Record W2954328562 · doi:10.4236/ajps.2019.106078

Potential Roles of Fatty Acids and Lipids in Postharvest Needle Abscission Physiology

2019· article· en· W2954328562 on OpenAlexaff
Gaye E. MacDonald, Rajasekaran R. Lada, C. D. Caldwell, Chibuike C. Udenigwe, Mason T. MacDonald

Bibliographic record

VenueAmerican Journal of Plant Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides and Plant Cell Walls
Canadian institutionsUniversity of OttawaDalhousie University
Fundersnot available
KeywordsAbscissionPostharvestBiologyBotanyChemistryBiochemistry

Abstract

fetched live from OpenAlex

Our understanding of postharvest needle abscission physiology in conifers has greatly improved in the last decade. Abscission is initially triggered by root detachment, which begins a cascade of changes such as decreased water uptake, water potential, and auxins and increased membrane injury, ethylene, abscisic acid, volatile terpenes, and catalytic enzymes. Needle abscission is also affected by environmental factors. For example, a period of cold acclimation generally delays postharvest abscission. The aforementioned pieces of evidence, along with previous studies, strongly points to a role for plant lipids and fatty acids. Studies from other species have pointed out key roles in abscission and stress responses for a variety of phospholipids and galactolipids, which has not been studied in balsam fir. It is imperative to have an understanding of the role of plant lipids and fatty acids to further our overall understanding of the physiological mechanisms of postharvest abscission and needle abscission resistance. This review is an overview of membrane lipids and fatty acids, changes that occur postharvest and the interaction that lipids may have in the phenomenon of postharvest abscission.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.132

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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 designObservational
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

Citations14
Published2019
Admission routes1
Has abstractyes

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