Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy
Bibliographic record
Abstract
Near-infrared (NIR) spectroscopy is a rapid nondestructive technique that has been used to characterize chemical and physical properties of a wide range of materials. In this study, transmittance NIR spectra from thin wood wafers cut from increment cores were used to develop calibration models for the estimation of α-cellulose content, average fiber length, fiber coarseness, and lignin content in the laboratory. Eleven-year-old trees from two sites were sampled using 12-mm increment cores. Earlywood and latewood of ring 3 and ring 8 from these samples were analyzed in the laboratory using microanalytical methods for α-cellulose content, average fiber length, fiber coarseness, and lignin content. NIR calibrations and laboratory measurements based on one site were generally reliable, with coefficients of determination (R2) ranging from 0.54 to 0.88 for average fiber length and α-cellulose content, respectively. Predicting ring 8 properties using ring 3 calibration equations showed potential for predicting α-cellulose content and fiber coarseness, with R2 values of approximately 0.60, indicating the potential for early selection. Predicting the wood properties using the calibration equations from one site to predict another showed moderate success for α-cellulose content (R2 = 0.64) and fiber coarseness (R2 = 0.63), but predictions for fiber length were relatively poor (R2 = 0.43). Prediction of lignin content using transmittance NIR spectroscopy was not as reliable in this study, partially because of low variation in lignin content in these wood samples and large errors in measuring lignin content in the laboratory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".