Evaluating electrical resistivity tomography and crown surface area to estimate leaf area of sugar maple and yellow birch
Bibliographic record
Abstract
Abstract Sapwood area is an important parameter for estimating transpiration at whole‐tree and watershed scales, given that it is used for scaling up point sap flow measurements. However, its determination may be difficult, particularly for large‐diameter trees of diffuse‐porous, broad‐leaved species. By delineating only the highly conductive sapwood, electrical resistivity tomography (ERT) may be more appropriate than sapwood dyeing for estimating tree sapwood area. We compared sapwood area measurements made with ERT with those that were obtained from wood dyeing, assuming that the more adequate measure was that which was best correlated with tree leaf area. To achieve this objective, we sampled 31 sugar maple ( Acer saccharum Marsh.) and 20 yellow birch ( Betula alleghaniensis Britt.) trees covering a large range of tree diameters and leaf areas from one site located in eastern Canada. Also, 79 sugar maple trees were sampled in two other sites to document the estimation of tree leaf area over a large territory. The sapwood area that was determined from ERT appeared to be the best method for scaling up point sap flow measurements to whole‐tree transpiration. The predictive ability of ERT was particularly greater than that of wood dyeing when they were both measured at 1 m. If the objective is to estimate tree leaf area rather than sapwood area, the crown surface area would provide a useful substitute for the ERT method because it is rapid and easy to measure while serving a robust predictor applicable over a large territory.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".