Functional chemical, physical, and mechanical traits of Amazonian forest species from different ecological groups using NIR spectroscopy
Bibliographic record
Abstract
The variation in xylem properties may reflect the ecological functional strategies of each species. In this study, we evaluated the anatomical characteristics and quantified the chemical, physical, and mechanical traits of the wood of Simarouba amara (marupá), Scleronema micranthum (cardeiro), and Manilkara huberi (maçaranduba), species belonging to different ecological groups, using near-infrared (NIR) spectroscopy. The samples were collected from a terra-firme forest in Amazonas/Brazil, and 10 cm thick discs were extracted from the tree trunks at breast height. The samples were prepared to obtain transverse, radial, and tangential NIR spectra. The wood of Simarouba amara (low density) presented an anatomical profile with vessels measuring 253 ± 23 µm in diameter, a frequency of 3.65 ± 1.22 vessels/mm2, a low extractive content (1.98 ± 0.55%), and a low modulus of elasticity. In contrast, M. huberi (high density) exhibited the opposite profile, characterized by a high vessel frequency (13.32 ± 0.34 vessels/mm2), relatively small vessel diameters (99 ± 2 µm), high extractive content, and high modulus of elasticity and modulus rupture. The wood of Scleronema micranthum was notable for its higher content of polyphenols (11.83 ± 2.51%), holocellulose (59.12 ± 2.69%), and a medium density (0.65 ± 0.04 g/cm3). The results of the wood characteristics assessed in this study reinforce the reliability of NIR spectroscopy as a robust tool for estimating the traits of different ecological groups of tropical species.
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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 | 0.000 |
| 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) | 0.001 | 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".