Grapevine leaf water potential based upon near infrared spectroscopy
Bibliographic record
Abstract
Leaf water potential is a measure commonly used to describe crop water status and water stress dynamics. The established method for determining leaf water potential using a pressure chamber is cumbersome and subject to operator error as well as time/temperature limitations. These limitations prohibit the intensive sampling required to support proactive water management of commercial crops, including vineyards. Particular for grapevines there is need for faster, more precise and more reliable tools for determining leaf water potential in the field. Portable Near-infrared spectroscopy and multivariate data analysis were applied for the modeling and prediction of leaf water potential in grapevines. For field-grown wine grapes the most significant and intensive leaf absorptions occurs in the region from 1440 to 1950 nm and again beyond 2,200 nm. Multivariate analysis of these spectra, referenced against pressure chamber measurements as a standard, showed correlation coefficients from 0.87 to 0.95 clearly demonstrated that this technology can provide a fast and reasonable assessment of leaf water potential in the field.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".