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Record W2357841266

Application of Microdialysis Technique in Physiology and Biochemistry of Plant Research

2013· article· en· W2357841266 on OpenAlexaff
Jinlong Ma

Bibliographic record

VenueHubei nongye kexue · 2013
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsMicrodialysisCapillary electrophoresisAnalyteSampling (signal processing)ChromatographyChemistryComputer scienceBiological systemBiochemical engineeringIn vivoBiotechnologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Microdialysis is a sampling technique that can be employed to monitor biological events both in vivo and in vitro,it can be coupled with a variety of analytical instruments,can provide monitoring information for biological active substances changed with time and concentration in other aqueous environment or outside the cells dynamically in realtime.It is advantageous in fast,selective and sensitive analysis while preserving temporal information without affecting the growth of organisms.At the same time,the changes of analyte can be detected immediately in external environment.Furthermore,microdialysis samples without pretreatment,which are coupled with high-precise analytical systems,will realize truly real-time,online and cheap tracking detection.Although microdialysis sampling focusing on intercellular matrix has been applied in animals and human,it has not been extensively employed to detect various material changes in plant apoplast.So microdialysis sampling technique can overcome the bottleneck of previous research on plants.An overview of microdialysis system about principle,probe,membrane and parameters,furthermore sampling for plants and analytical methods employed for online analysis,including gas chromatography(GC),high performance liquid chromatography(HPLC),capillary electrophoresis(CE),and so on,were reviewed.

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 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 score0.004
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.224 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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