Global near infrared spectroscopy models to predict wood chemical properties of <i>Eucalyptus</i>
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
Global near infrared spectroscopy models (multiple-species, multiple-sites) were developed to predict chemical properties of Eucalyptus wood. The sample data set included 186 samples from four data sets (five species) originating from six countries: Eucalyptus urophylla from Argentina, Colombia, Venezuela, and South Africa; Eucalyptus dunnii from Uruguay; Eucalyptus globulus and Eucalyptus nitens from Chile; and Eucalyptus grandis from Colombia. The 186 samples were all preselected from larger collections of 400 to nearly 1800 samples to represent the range of chemical and spectral variation in each data set. The chemical traits modeled were total lignin, insoluble lignin, soluble lignin, syringyl–guaiacyl ratio (S/G), glucose, xylose, galactose, arabinose, and mannose. Single-species models and global multiple-species models were developed for each chemical constituent. For the global model, the R 2 cv for total lignin, insoluble lignin and syringyl–guaiacyl ratio were 0.95, 0.96, and 0.86, respectively. An alternate expression of the syringyl–guaiacyl relationship (S/(S+G)) resulted in better near infrared calibrations (e.g., for the global model, R 2 cv = 0.95). The global models for sugar content were also very good, but were slightly inferior to those for the lignin related traits, with R 2 cv = 0.74 for glucose, 0.89 for xylose, and from 0.72 to 0.91 for the minor sugars. To investigate the utility of the global models to predict chemical traits for species not included in the calibration, three-species calibrations were used to predict each trait in a fourth species data set. The prediction fit statistics ranged from excellent to poor depending on the species and trait, but in general the predictions would be at least moderately useful for most species-trait combinations. For some species-trait combinations with poor initial predictions from the global model, the inclusion of 10 samples from the “new” species into the calibration global model improved the fit statistics substantially. The global calibrations will be useful in tree breeding programs to rank species, families, and clones for important wood chemical traits.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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