Near-infrared spectral signatures differentiate blue stain and brown rot fungi in conifer and broadleaf trees
Why this work is in the frame
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Bibliographic record
Abstract
Colonization by blue stain and brown rot fungi affects timber quality in distinct ways. Blue stain fungi cause discoloration without reducing wood properties, while brown rot fungi degrade wood tissues, resulting in brittleness and brown coloration. Given these chemical differences, we investigated whether near-infrared spectroscopy (NIRS) could distinguish between these fungal types. We hypothesized that early fungal attack would produce unique spectral signatures, allowing for rapid identification. Wood disc samples were collected from white spruce, lodgepole pine, and trembling aspen in Fox Creek, northwest Alberta, Canada, ca. 4 months after a wildfire. The trees were colonized by fungi associated with blue and brown sapwood discoloration and analyzed using NIRS. In white spruce, we found consistent and significant absorbance differences between blue- and brown-discolored sapwood across each 100 nm segment. In lodgepole pine, the most distinct differences occurred in the 1650–1750 nm, 2050–2150 nm, and 2350–2450 nm ranges. For trembling aspen, differences were evident across most 100 nm intervals, except 2150–2250 nm. Permutational multivariate analysis of variance (PERMANOVA) indicated greater spectral dissimilarity between fungal types in white spruce and trembling aspen, with less pronounced differences in lodgepole pine. Our findings suggest that NIRS can effectively classify fungal-discolored wood in white spruce and trembling aspen within the first year following wildfire. However, its application to lodgepole pine in the same timeframe may be less reliable.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it