Preparation of soybean seed samples for FT-IR microspectroscopy
Bibliographic record
Abstract
Typical preparation of seed samples for infrared (IR) microspectroscopy involves imbibition of the seed for varying time periods followed by cryosectioning. Imbibition, however, may initiate germination even at 4 degrees C with associated changes in the chemistry of the sample. We have found that it is possible to section seeds that are sufficiently hard, such as soybeans, on a standard laboratory microtome without imbibition. The use of dry sectioning of unimbibed seeds is reported here, as well as a comparison of different mounting media and modes of analysis. Glycerol, Tissue-Tek, and ethanol were used as mounting media, and the quality of the resulting spectra was assessed. Ethanol was the preferred mountant, because it dried quickly with no residue and thus did not interfere with the spectrum of interest. Analysis in transmission mode using barium fluoride windows to hold the samples was compared with transmission-reflection analysis with sections mounted on special infrared-reflecting slides. The two modes of analysis performed well in different regions of the spectrum. The mode of analysis (transmission vs. transmission-reflection) should be based on the components of greatest interest in the sample.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".