Root and leaf production, mortality and longevity in response to soil heterogeneity
Bibliographic record
Abstract
Summary Patches of fertile soil support concentrations of roots, but whether this reflects increased production or increased longevity is not known. We examined the production and longevity of roots of the grass Festuca rubra in response to soil heterogeneity. We also explored the extent to which root dynamics reflect shoot responses to heterogeneous soils. Root and leaf dynamics were followed in pots of heterogeneous or homogeneous soils containing the same total amount of nutrients. Digital minirhizotron images of roots and leaves were collected weekly. Root length was significantly greater in homogeneous than heterogeneous soils. This was caused by significantly larger root production but shorter life span. In contrast, soil heterogeneity had no effect on leaf production or longevity. Within heterogeneous pots, root and leaf production were strongly concordant, both being significantly greater in fertilized patches. More roots died in fertilized patches, but leaf mortality was not affected. Longevity of neither roots nor leaves was affected by the location of a fertile patch. Spatial variation in production of roots and shoots in response to nutrient patches was concordant. Roots and shoots, however, showed independent responses to the presence of within‐pot heterogeneity. A decrease in total root length in heterogeneous soils was a counterintuitive result of decreased production and increased longevity.
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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) | 0.000 | 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".