Root system influence on high dimensional leaf phenotypes over the grapevine growing season
Bibliographic record
Abstract
Summary In many perennial crops, grafting the root system of one individual to the shoot system of another individual has become an integral part of propagation performed at industrial scales to enhance pest, disease, and stress tolerance and to regulate yield and vigor. Grafted plants offer important experimental systems for understanding the extent and seasonality of root system effects on shoot system biology. Using an experimental vineyard where a common scion ‘Chambourcin’ is growing ungrafted and grafted to three different rootstocks, we explore associations between root system genotype and leaf phenotypes in grafted grapevines across a growing season. We quantified five high-dimensional leaf phenotyping modalities: ionomics, metabolomics, transcriptomics, morphometrics, and physiology and show that rootstock influence is subtle but ubiquitous across modalities. We find strong signatures of rootstock influence on the leaf ionome, with unique signatures detected at each phenological stage. Moreover, all phenotypes and patterns of phenotypic covariation were highly dynamic across the season. These findings expand upon previously identified patterns to suggest that the influence of root system on shoot system phenotypes is complex and broad understanding necessitates volumes of high-dimensional, multi-scale data previously unmet.
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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 | 0.001 | 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".