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 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) | 0.000 | 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 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".