A Non-invasive and Real-time Monitoring of the Regulation of Photosynthetic Metabolism Biosensor Based on Measurement of Delayed Fluorescence in Vivo
Bibliographic record
Abstract
In this paper, a new principle biosensor for non-invasive monitoring of theregulation of photosynthetic metabolism based on quantitative measurement of delayedfluorescence (DF) is developed. The biosensor, which uses light-emitting diode lattice asexcitation light source and a compact Single Photon Counting Module to collect DF signal,is portable and can evaluate plant photosynthesis capacity in vivo. Compared with itsprimary version in our previous report, the biosensor can better control environmentalfactors. Moreover, the improved biosensor can automatically complete the measurements oflight and CO2 response curves of DF intensity. In the experimental study, the testing of theimproved biosensor has been made in soybean (Glycine max Zaoshu No. 18) seedlingstreated with NaHSO3 to induce changes in seedlings growth and photosynthetic metabolism.Contrast evaluations of seedlings photosynthesis were made from measurements of netphotosynthesis rate (Pn) based on consumption of CO2 in tested plants. Current testingresults have demonstrated that the improved biosensor can accurately determine theregulatory effects of NaHSO3 on photosynthetic metabolism. Therefore, the biosensorpresented here could be potential useful for real-time monitoring the regulatory effects ofplant growth regulators (PGRs) and other exogenous chemical factors on plant growth andphotosynthetic metabolism.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".