A high-throughput approach for quantifying turgor loss point in grapevine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and drought response traits in grape vine have long been the focus of viticultural research. Turgor loss point ( π tlp ) is gaining attention as an indicator of drought tolerance in plants, though estimating π tlp often requires the construction and analysis of pressure-volume (P-V) curves which are very time consuming. While P-V curves remain a valuable tool for assessing π tlp and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating π tlp , especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in π tlp across and within 12 clones of grape vine ( Vitis vinifera subsp. vinifera ) and one wild relative ( Vitis riparia ), and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4–5% of the variation in π tlp while clone/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific π tlp variation in grape vine. Also at the leaf level, π tlp measured using a dewpoint hygrometer approximated π tlp values ( r 2 = 0.254) and conserved π tlp rankings from P-V curves (Spearman’s ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t -test p = 0.01), average difference in π tlp for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/clone level, estimates of π tlp measured by the two methods were also statistically correlated ( r 2 = 0.304), did not deviate statistically from a 1:1 relationship, and conserved π tlp rankings across clones (Spearman’s ρ = 0.692). The dewpoint hygrometer (taking ∼ 10–15 min on average per measurement) captures fine-scale intraspecific variation in π tlp , with results that approximate those from P-V curves (taking 2–3 h on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in π tlp , and potentially greatly increasing replication when estimating this drought tolerance trait in grape vine and other crops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it