Application of Microdialysis Technique in Physiology and Biochemistry of Plant Research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".