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Record W4396698192 · doi:10.1093/aob/mcae067

Screening methods for thermotolerance in pollen

2024· review· en· W4396698192 on OpenAlexafffund
Madeleine Stokes, Anja Geitmann

Bibliographic record

VenueAnnals of Botany · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Reproductive Biology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiologyPollenBotany

Abstract

fetched live from OpenAlex

Plant reproduction is highly susceptible to temperature stress. The development of the male gametophyte in particular represents a critical element in the reproductive cycle with high sensitivity to elevated temperatures. Various methods have been used to test the effect of temperature stress on pollen performance or to determine the degree of susceptibility of given species and genotypes. The information gained informs the development of new crop varieties suited to grow under warmer conditions arising through climate change and facilitates predicting the behaviour of natural populations under these conditions. The characterization of pollen performance typically employs the terms 'pollen viability' and 'pollen vigour', which, however, are not necessarily used consistently across studies. Pollen viability is a nominal parameter and is often assayed relying on cellular features as proxy to infer the capability of pollen grains to germinate and complete double fertilization. Alternatively, pollen germination can be determined through in vitro growth assays, or by monitoring the ability of pollen tubes to complete different progamic steps in vivo (ability to reach an ovule, release sperm cells, lead to seed set). Pollen vigour is an ordinal parameter that describes pollen tube growth rate or the efficiency of pollen tube growth as inferred by its morphology or growth pattern. To ensure consistent and relevant terminology, this review defines these terms and summarizes the methodologies used to assess them.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.185
GPT teacher head0.504
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2024
Admission routes2
Has abstractyes

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