Electrical properties of temperate forest trees: a review and quantitative comparison with vines
Bibliographic record
Abstract
Trees form the terrestrial interface with the atmosphere in forested regions. The electrical properties of trees may influence their response to atmospheric conditions and potentially lethal phenomena (e.g., lightning). We review the literature describing electrical properties of trees and provide a tabular summary of the methods and goals of each study. We hypothesized that electrical resistivity varies consistently among species and between growth forms. We surveyed resistivity of eight tree and three vine species in Michigan and Kentucky, and we quantified resistivity over a moisture gradient for wood blocks of four tree species. Resistivity varied predictably with stem diameter and differed among species and growth forms. Specifically, resistivity of trees was approximately 200% higher than resistivity of vines, and resistivity of conifers was 135% higher than that of hardwoods. The regional comparison showed no difference in resistivity of red maple (Acer rubrum L.) and sugar maple (Acer saccharum Marsh.) between Michigan and Kentucky. These results, in combination with interspecific differences observed among wood blocks, suggest that there is a phylogenetic basis for variation in resistivity that reflects differences in anatomy and physiology. Our review and empirical survey provide a framework for studying the ecological effects of lightning in the context of the electrical properties of trees.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".