CHEMOTYPING USING SYNCHROTRON MID-INFRARED AND X-RAY SPECTROSCOPY TO IMPROVE AGRICULTURAL PRODUCTION
Bibliographic record
Abstract
Synchrotron techniques are powerful tools in material and environmental sciences; however, they are currently underutilized in plant research. The Canadian Light Source synchrotron at the University of Saskatchewan campus is the only such facility in Canada open to academic, government, and industrial clients. This review introduces the potential of synchrotron-based spectroscopic methods and its applications to agriculture and plant sciences. Relative ease of sample preparation, nondestructive analysis, high spatial resolution, and multiple response measurements within a single sample are among its advantages. Synchrotron-based Fourier transform mid-infrared spectromicroscopy, X-ray absorption, and fluorescence spectromicroscopy are included in the several approaches discussed. Examples range from evaluating protein secondary structure and nondestructive compositional analysis of leaf epicuticular wax and pollen surface lipids to cell wall composition and nutrient analyses. Synchrotron technology can help to initially identify key spectra related to plant properties for subsequent higher throughput techniques. One example is the adaptation of synchrotron techniques for lower resolution analysis in the field such as nondestructive elemental analysis for localization of nutrients in fruit crops using handheld high-throughput devices. In addition, interest in creating high-throughput systems based on synchrotron technology itself is driving the development of new hardware to meet these larger challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".