Multiple responses of fine root resource uptake strategies to gravel content in a subtropical plantation
Bibliographic record
Abstract
Most forest soils contain substantial amounts of gravel. However, unlike the more widely known root resource uptake behaviors which respond to resource patches in substrate without gravels, how roots respond to substrate containing different gravel levels is poorly understood. We grew roots in substrates with five gravel levels (0%, 10%, 20%, 30%, and 40% of volume) in a subtropical Schima superba Gardn. et Champ plantation, determined fine root dynamics and turnover rate with minirhizotrons, measured fine root morphological, architectural, mycorrhizal colonization, chemistry, and mass allocation. The presence of gravel in the substrate delayed the timing of peak root growth. In the substrate with higher gravel content, plants produced more in roots in autumn, but there were fewer roots in summer and the roots tended to exhibit lower fine root turnover rate and mycorrhizal colonization, but higher root biomass allocation. The higher root biomass in the substrate with higher gravel content was associated with higher root carbon/nitrogen ratio. Our findings emphasize the complexity of root resource uptake behavior in response to gravel content and suggest that incorporating substrate gravel content into root studies may help to improve the prediction of patch exploitation and nutrient acquisition in stony soils.
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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".