Electric resistivity tomography shows radial variation of electrolytes in Quercus robur
Bibliographic record
Abstract
Electric resistivity tomograms of English oak ( Quercus robur L.) show a very distinct pattern of electric resistivity that has not been found in any other tree species yet and that cannot be related to the distribution of wood moisture content over the stem cross section. To reveal the factors underlying this two-dimensional pattern of electric resistivity, the variation of specific gravity and wood moisture content was analyzed in 18 cross sections of six roadside English oak trees after electric resistivity tomography. pH and electrolyte content were analyzed in two representative cross sections. Results show that electric resistivity correlates neither with wood moisture content nor density. The steep increase in electric resistivity at the sapwood–heartwood boundary correlates well with decreasing pH, potassium, and magnesium. The decreasing electric resistivity within the heartwood of English oak correlates with potassium and magnesium, increasing from the sapwood–heartwood boundary to the pith. More research is needed to identify species-specific electric resistivity patterns and their main factors if the method is to be used to detect wood fungal decay, historical ground water contamination, or other influences that may change the pattern of electric resistivity in the stem cross section.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".