‘Chardonnay’ paired with leaf economics traits and a side of soil compaction
Bibliographic record
Abstract
Functional trait variation in plants of the same species or genotype are a critical determinant of ecosystem processes, especially in agroecosystems where single crop species or genotypes exist in very high abundances. Yet to date only a small number of studies have evaluated if, how, or why traits forming the Leaf Economics Spectrum (LES) vary within crops, despite such studies informing our understanding of: 1) the environmental factors that drive crop LES trait variation; and 2) how domestication has altered LES traits in crops vs. wild plants. We assess intragenotype variation in LES traits in wine grape variety ‘Chardonnay’ (Vitis vinifera)—among the world’s most commercially important crops, across a soil compaction gradient: one of the most prominent characteristics of agricultural soils that may drive crop trait variation. ‘Chardonnay’ traits covary along an intragenotype LES in patterns that were qualitatively similar to, though statistically distinguishable from, those observed among wild plants: resource acquiring vines expressed a combination of high mass-based photosynthesis (Amass), mass-based dark respiration (Rmass), leaf nitrogen concentrations (N), coupled with low leaf mass per area (LMA); the opposite set of trait values defined the resource conserving end of the ‘Chardonnay’ LES. Traits related to resource acquisition (Amass, Rmass, and leaf N) declined with greater bulk density, while traits related to investment in leaf construction costs (LMA) increased with greater bulk density. Compared to wild plants, ‘Chardonnay’ expressed lower Rmass for a given rate of Amass, and an unexpected positive covariation between leaf carbon (C) concentrations and Rmass, Amass, and leaf N. Our findings uncover a deeper understanding of both the domestication syndromes in grapevines, and expand our understanding of trait-based crop responses to environmental change and gradients.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".