Assessment of seed germinability of mechanically-damaged soybeans using near-infrared hyperspectral imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mechanical damage of seeds during harvesting and postharvest handling affects their germinability, which directly impacts crop yield. The feasibility of near-infrared (NIR) hyperspectral imaging technique was studied to predict the germinability of soybeans in a rapid and non-destructive way. Soybean seeds were artificially damaged using an impact test device, and the effect of three levels of impact energy (0.12, 0.22 and 0.32 J) was studied on the germination rates of these seeds at four moisture contents (11, 13, 15 and 17%, wet basis). A multivariate statistical model (partial least squares discriminant analysis (PLSDA)) was built using the NIR spectra of the soybean samples to classify them based on their germinability. The prediction performance of the PLSDA model was defined based on its accuracy (0.94), sensitivity (0.89) and specificity (0.91). The model showed good performance in terms of accuracy and sensitivity for pre-defined classes of soybean samples, establishing that the non-destructive NIR hyperspectral imaging technique could be used to predict soybean seed germination.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it