Grapevine leaf water potential based upon near infrared spectroscopy
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
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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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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