NIR spectral information used to predict water content of pine seeds from multivariate calibration
Bibliographic record
Abstract
It is possible to predict the water content (6%32%) of pine seeds single seeds at 30 degree-days and bulk samples at 45 degree-days using multivariate calibration models based on the near infrared (NIR) spectra (11002200 nm) of the seeds. One would expect the water peaks in the NIR spectra to contribute uniquely to the models, but this is not entirely the case. Different ways of studying models and spectra reveal that the contribution of the spectral information to the calibration models is complicated and nonlinear. Model parameters also show contributions from the biological processes in the seeds. Regression models between water content and NIR spectra using biorthogonal partial least squares (BPLS) showed that water content was associated with overtones of HOH, mainly around the peak of water at 19301940 nm, as expected. However, overtones of CH, C=O, and NH also influenced the BPLS models. This was caused by evolving biological phenomena, such as respiration and protein metabolism in imbibed seeds, and hence gave more complex regression models of seedwater interaction. There was also a difference in NH absorption that indicated enhanced protein metabolism at prolonged degree-days.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".