Substituting Stem's Water Content by Electrical Conductivity for Monitoring Water Status Changes
Bibliographic record
Abstract
Rapid and sensitive detection of stress in trees due to irrigation practices, draught, salinity, pollution, lack of nutrients, or diseases may be useful for research and practical purposes. Tree stress could be monitored by following changes in wood water content via time domain reflectometry (TDR). We have searched for a user‐friendly and less expensive tool because, although TDR is perhaps the most suitable method, it is too expensive and complicated for everyday use. The objective of this study was to understand the relations between the electrical conductivity (σ stem ) and water content (θ stem ) in tree stem segments of seven species with TDR probes installed. By leaching stem segments with salt solutions or air we were able to change the salinity and water content independently. We have shown that (i) σ stem is more sensitive to changes in θ stem than to changes in salinity of the sap, and (ii) 30‐mm‐long rods on the TDR probe can sensitively and accurately measure θ stem We propose that σ stem changes might be used as a proxy for changes in stem water content or stem water potential. Hence, electrical resistivity measurements may substitute for water content measurements with the following advantages: improved accuracy, higher flexibility in probe construction, application to stem diameters <30 mm, and significantly lower costs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".