Vertical distribution of fine-root area in relation to stand age and environmental factors in black locust (<i>Robinia pseudoacacia</i>) forests of the Chinese Loess Plateau
Bibliographic record
Abstract
To examine the spatiotemporal characteristics of the distribution of fine-root area and its relationship with stand age and environmental factors in black locust (Robinia pseudoacacia L.) on the Chinese Loess Plateau, black locust stands were selected at four sites along a precipitation gradient. Four stands of different ages and a transect along the hillslope were also selected at one of the sites. With increasing stand age, fine-root area at the tree level increased exponentially, and the rooting pattern tended to be deeper for trees up to 15 years old and then shallower thereafter. The temporal changes of fine-root distribution could be quantified using stand age and soil nutrients. At the hillslope scale, fine-root area index (FRAI) was lower while the rooting pattern was deeper in the middle slope than in the upper and lower slopes, and the fine-root distribution could be quantified using elevation and soil properties. At the regional scale, FRAI decreased substantially while exhibiting similar rooting patterns with decreasing soil water and nutrient availability along the precipitation gradient. Humidity index represented the regional environmental variation and could be used to quantify FRAI. These findings will be helpful for improving quantification of fine roots and enhancing the accuracy of ecohydrological models.
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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 | 0.000 |
| 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".