Deep soil exploration vs. topsoil exploitation: distinctive rooting strategies between wheat landraces and wild relatives
Bibliographic record
Abstract
AIMS: Diversity of root systems among genetic resources can contribute to optimize water and nutrient uptake. Topsoil exploitation vs. deep soil exploration represent two contrasting ideotypes in relation to resource use. Our study reveals how rooting patterns changed between wheat wild progenitors and landraces in regard to these ideotypes. METHODS: Root (partitioning, morphology, distribution, elongation, anatomy) and shoot traits (dry-matter, leaf area, assimilation) of durum landraces, wild emmer and wild einkorn from Iran, Syria, Turkey and Lebanon were phenotyped using the GrowScreen-Rhizo platform. Distinctive rooting patterns were identified via principal component analysis and relations with collection site characteristics analyzed. RESULTS: Shoot trait differentiation was strongly driven by seed weight, leading to superior early vigor of landraces. Wild progenitors formed superficial root systems with a higher contribution of lateral and early-emerging nodal axes to total root length. Durum landraces had a root system dominated by seminal axes allocated evenly over depth. Xylem anatomy was the trait most affected by the environmental influence of the collection site. CONCLUSIONS: -type wild einkorn was most similar to a topsoil exploiting strategy with potential competitive advantages for subsistence in natural vegetation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11104-020-04794-9.
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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".