Testing models for the leaf economics spectrum with leaf and whole-plant traits in<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
The leaf economics spectrum (LES) describes strong relationships between multiple functional leaf traits that determine resource fluxes in vascular plants. Five models have been proposed to explain these patterns: two based on patterns of structural allocation, two on venation networks and one on resource allocation to cell walls and cell contents. Here we test these models using data for leaf and whole-plant functional traits. We use structural equation modelling applied to multiple ecotypes, recombinant inbred lines, near isogenic lines and vascular patterning mutants of Arabidopsis thaliana that express LES trait variation. We show that a wide variation in multiple functional traits recapitulates the LES at the whole-plant scale. The Wright et al. (2004) model and the Blonder et al. (2013) venation network model cannot be rejected by data, while two simple models and the Shipley et al. (2006) allocation model are rejected. Venation networks remain a key hypothesis for the origin of the LES, but simpler explanations also cannot be ruled out.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".