Differential impact of commercial rootstocks on the physiological response of a common walnut scion to drought stress
Bibliographic record
Abstract
Walnut rootstocks are commonly used in California orchards to provide resistance to soil-borne pests and diseases. However, little information exists about the impact of commercial rootstock on the common scion's physiological response under drought. This is becoming increasingly important since walnuts are commonly cultivated in semi-arid regions where frequent and severe droughts require efficient water use. We previously reported that own-rooted walnut rootstocks (RX1, VX211 and Vlach) differ in their physiological performance under drought. Here, we evaluated whether similar water relations and performance are conferred to a common English walnut scion (Juglans regia cv. Cisco). To do so, we used a mini-lysimeter platform to continuously track soil moisture and transpirational water loss from trees. Along with the canopy's estimated leaf area, changes in canopy shape and texture were evaluated using deep learning as an independent method to analyze canopy response to water stress. In support of our recent findings, the scion grafted onto rootstock RX1 exhibited subtle improvements in physiological performance associated with higher transpiration and canopy conductance under well-watered condition compared to Vlach and VX211 rootstocks. Canopy conductance, texture, and shape were not significantly affected by rootstock under water stress. However, Cisco grafted onto RX1 exhibited higher leaf turgor and water use efficiency, and lower osmotic potentials under water stress. Our results suggest some subtle differences in water relations between the rootstock genotypes, and propose an efficient deep-learning method to screen canopies for water stress-induced response through image processing.
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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 | 0.000 |
| 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 | 0.001 |
| 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".