Rapid analysis of the microfibril angle of loblolly pine from two test sites using near-infrared analysis
Bibliographic record
Abstract
There have been several recent studies employing near infrared (NIR) spectroscopy for the rapid determination of microfibril angle (MFA). However, only a few have utilized samples cut from individual rings of increment cores, and none have been as large as this present study, sampling over 600 trees from two test sites producing over 3000 individual ring samples for MFA analysis. This has allowed the use of individual growth ring models rather than using those based on earlywood, latewood, corewood or outerwood. It was observed that for both test sites, the strongest models were from the “All”, earlywood and latewood sample sets. The individual growth ring calibration models provided poorer RPD values despite using over 200 samples in the analyses. In general, the results from the test samples largely mirrored those from the corresponding calibration samples. Corresponding test sample predictions from the opposing site were noticeably poorer than test samples from the same site. Thus, a greater variation in the number of sites would provide improved model robustness. This study has found that the models based on individual ring samples were not as strong as those obtained in other studies based on the radial-longitudinal face of wood strips, spread over several growth rings.
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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) | 0.000 | 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 | 0.000 |
| 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 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".