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Record W7031980984

植物細胞における小胞体凍結動態の観察

2012· article· en· W7031980984 on OpenAlexaboutno aff

Bibliographic record

VenueIwate University Repository (Iwate University) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsnot available
Fundersnot available
KeywordsEndoplasmic reticulumVesicleCalciumExtracellularPlant cellFluorescence microscopeEpidermis (zoology)
DOInot available

Abstract

fetched live from OpenAlex

Many plants living under subzero temperatures in winter increase freezing tolerance by exposure to non-freezing temperature, which is known as cold acclimation. In cold-acclimated cells, unique cryobehaviors of the plasma membrane and endoplasmic reticulum (ER) have been reported but their physiological meaning or mechanism is largely unknown. Allium fistulosumis a cold-hardy Welshonion which survives winter of-40℃ in Saskatchewan, Canada, andintact cells inthe single epidermal layer, which is easily peeled from leaf sheath, were observed. The cryobehavior of ER in these epidermal cells that were stained with ER-selective fluorescent dye (ER-Tracker) was observed using a confocal fluorescent microscope with cryostage. According to our observations, cold acclimation increased ER volume per cell and extracellular freezing induced ER vesiculation through the breakdown of the ER network. Freeze-induced ER vesicles in cold-acclimated cells were larger and more abundant than those in non-acclimated cells. ER vesiculation may be associated with extracellular calcium because freeze-induced ER vesicles tended to be more abundant in the presence of calcium than in the absence of calcium. Furthermore, ER vesiculation also occurred in Arabidopsis root cells, suggesting a possibility that ER vesiculationis conserved in monocotyledonous and dicotyledonous plants. After thawing, the ER network was recovered only in cold-acclimated cells, suggesting that the dynamics of ER during freeze/thaw cycles are associated with freezing tolerance.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.241
Teacher spread0.214 · 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.

Study designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

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Same venueIwate University Repository (Iwate University)Same topicArtificial Intelligence in LawFrench-language works237,207