Magnetic resonance imaging of water ascent in embolized xylem vessels of grapevine stem segments
Bibliographic record
Abstract
Wang, M., Tyree, M. T. and Wasylishen, R. E. 2013. Magnetic resonance imaging of water ascent in embolized xylem vessels of grapevine stem segments. Can. J. Plant Sci. 93: 879–893. Temporal and spatial information about water refilling of embolized xylem vessels and the rate of water ascent in these vessels is critical for understanding embolism repair in intact living vascular plants. High-resolution1H magnetic resonance imaging (MRI) experiments have been performed on embolized grapevine stem segments while they were subjected to refilling at two different applied water pressures in order to investigate these important aspects of embolism repair. Magnetic resonance imaging difference images show that vessels located near the bark tend to refill faster than do inner ones, suggesting that vessel position within the cross section of the stem may affect the refilling process within the vessel. An MRI method for determining the water ascent velocity in each individual embolized xylem vessel is presented. At ambient pressure, the water ascent velocity ranges from 0.0090 to 0.60 mm min−1, but increases to a range of 0.016 to 0.70 mm min−1at 9.8 kPa above ambient pressure. A steady-state bubble model that offers analytical solutions of the water ascent velocity in embolized xylem vessels is presented; model calculations show that if other parameters are held constant, water ascent velocity is influenced by vessel diameter and position.
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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) | 0.001 | 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".