Zooming into refractory timber: enhancing anatomical identification with confocal laser scanning microscopy and fluorescence
Bibliographic record
Abstract
Summary Accurate wood identification is crucial for combatting the illegal logging and trade of forest products worldwide. However, certain challenges such as small specimen size, high wood density, and level of degradation can complicate the identification process. There is therefore a need to develop methods and use complementary techniques in forensic wood identification, particularly for difficult samples. This study utilized confocal laser scanning microscopy (CLSM) to detect and locate autofluorescence of axial parenchyma cells (APCs) to facilitate the identification of three challenging unknown wood specimens. These specimens posed difficulties for sectioning with a microtome due to brittleness, density, or small size constraints. CLSM results were compared with those from conventional light compound and stereomicroscopy. In our investigation, the three unknown wood samples were identified as Bobgunnia cf. fistuloides, Chlorocardium cf. rodiei, and Diphysa cf. carthagenensis. Specifically, CLSM confirmed the absence of apotracheal parenchyma in B. cf. fistuloides, which could not be determined using fluorescence stereomicroscopy. For D. cf. carthagenensis, CLSM’s fluorescence intensity highlighted axial parenchyma effectively, surpassing fluorescence stereomicroscopy. A notable advantage of our non-invasive CLSM method was its ability to examine smooth, unsectioned block surfaces, thereby enhancing preservation and ‘in-situ’ visualization of APCs. Our results combining autofluorescence and CLSM clearly offered superior resolution in observing APCs compared to traditional microscopy and can thus be applied for increasing confidence in wood species identification.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".