Evaluation of Thermal-Based Physiological Indicators for Determining Water Stress Thresholds in Drip- Irrigated 'Regina' Cherry Trees
Bibliographic record
Abstract
Abstract This work aimed to assess the performance of different thermal-infrared (TIR)-based physiological indicators (PI) as an alternative to the stem water potential (Ψs) and stomatal conductance (gs) for monitor the water status of grafted drip-irrigated 'Regina' cherry trees. In addition, we evaluated the usefulness of piecewise linear regression for finding PI thresholds that are important for post-harvest regulated deficit irrigation (RDI) management. With this purpose, an irrigation experiment was carried out in the post-harvest period. Trees were submitted to three Ψs-based water stress treatments: T0 (fruit grower management treatment, or control) (Ψs > -1.0 MPa, without-to-low water stress); T1 (low to mild water stress treatment = -1.0 > Ψs > -1.5 MPa); and T2 (mild-to-severe water stress treatment = -1.5 > Ψs > -2.0 MPa). The results indicated that the trees were more stressed in T2 than in T0. In the former, averages of Ψs and gs were -1.75 MPa and 372 mmol m -2 s -1 , whereas they were -1.56 MPa and 427 mmol m -2 s -1 in T0. The piecewise model allowed determining the water stress thresholds of almost all studied PI. The breakpoints yielded by this analysis indicated that trees at Ψs lower than -1.5 MPa had a gs lower than 484 mmol m -2 s -1 . These results also showed that TIR-based PI, whose equations incorporate a temperature normalization, are a better indicator of cherry tree water status than those without normalization. The derived TIR-based PI threshold values could be used as a reference for managing drip-irrigated 'Regina' cherry trees.
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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 | 0.007 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".