Directional Phytoscreening: Contaminant Gradients in Trees for Plume Delineation
Bibliographic record
Abstract
Tree sampling methods have been used in phytoscreening applications to delineate contaminated soil and groundwater, augmenting traditional investigative methods that are time-consuming, resource-intensive, invasive, and costly. In the past decade, contaminant concentrations in tree tissues have been shown to reflect the extent and intensity of subsurface contamination. This paper investigates a new phytoscreening tool: directional tree coring, a concept originating from field data that indicated azimuthal concentrations in tree trunks reflected the concentration gradients in the groundwater around the tree. To experimentally test this hypothesis, large diameter trees were subjected to subsurface contaminant concentration gradients in a greenhouse study. These trees were then analyzed for azimuthal concentration gradients in aboveground tree tissues, revealing contaminant centroids located on the side of the tree nearest the most contaminated groundwater. Tree coring at three field sites revealed sufficiently steep contaminant gradients in trees reflected nearby groundwater contaminant gradients. In practice, trees possessing steep contaminant gradients are indicators of steep subsurface contaminant gradients, providing compass-like information about the contaminant gradient, pointing investigators toward higher concentration regions of the plume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".