Sap flow through partially embolized xylem vessel networks
Bibliographic record
Abstract
Sap is transported through numerous conduits in the xylem of woody plants along the path from the soil to the leaves. When all conduits are functional, vessel lumen diameter is a strong predictor of hydraulic conductivity. As vessels become embolized, sap movement becomes increasingly affected by factors operating at scales beyond individual conduits, creating resistances that result in hydraulic conductivity diverging from diameter-based estimates. These effects include pit resistances, connectivity, path length, network topology, and vessel or sector isolation. The impact of these factors varies with the level and distribution of emboli within the network, and manifest as alterations in the relationship between the number and diameter of embolized vessels with measured declines in hydraulic conductivity across vulnerability to embolism curves. Divergences between measured conductivity and diameter-based estimates reveal functional differences that arise because of species- and tissue-specific vessel network structures. Such divergences are not uniform, and xylem tissues may diverge in different ways and to differing degrees. Plants regularly operate under nonoptimal conditions and contain numerous embolized conduits. Understanding the hydraulic implications of emboli within a network and the function of partially embolized networks are critical gaps in our understanding of plants occurring within natural environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".