Influence of hemlock woolly adelgid infestation on the physiological and reflectance characteristics of eastern hemlock
Bibliographic record
Abstract
The hemlock woolly adelgid (HWA) (Adelges tsugae Annand) is an invasive insect in the eastern United States. Since its initial detection in Richmond, Virginia, in 1951, HWA has spread to half of the eastern hemlock (Tsuga canadensis (L.) Carr.) natural range. Detection of early infestation symptoms via remote sensing requires the knowledge of the changes in reflectance resulting from physiological changes in the host as inflicted by the insect and the selection of equipment with the appropriate sensor characteristics. Laboratory-based reflectance measurements of infested and non-infested hemlock foliage collected from four sites in southern New Hampshire and Maine occurred biweekly over 6 months in 2012 and weekly over 5 weeks in 2013. Vegetation indices (red edge inflection point (REIP), normalized difference vegetation index (NDVI), moisture stress index (MSI), and near infrared (NIR) 3/1 ratio) were associated with concurrent chlorophyll and moisture content data. Infested first-year foliage contained greater concentrations of chlorophyll and moisture, resulting in reduced visible spectral reflectance, greater REIP and NDVI values, and lower MSI and NIR 3/1 values than non-infested foliage. Furthermore, fluorescence measurements indicated greater photosystem function during the early stages of infestation, suggesting a possible compensatory response by hemlock to infestation. Significant differences in reflectance between infested and non-infested foliage were observed in late June and July in the weeks immediately following HWA settlement on new growth. Implementing these observations during remote sensing mission planning may increase the likelihood of detecting early HWA infestation symptoms at landscape scales.
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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 | 0.000 | 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) | 0.001 | 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".