Potential replacement understory woody plants for <i>Robinia pseudoacacia</i> plantations: species composition and vertical distribution pattern
Bibliographic record
Abstract
Black locust ( Robinia pseudoacacia L.) plantations on the Loess Plateau have become multigenerational sprouting forests with an obvious trend toward degradation. The species composition and vertical distribution pattern of understory woody plants were investigated in mature stands located at the top (T_GS) and bottom (B_GS) of a slope in the gully region to explore whether there may be replacement species for black locust. The species composition of T_GS and B_GS clearly differed, and species diversity indices in B_GS were significantly greater than those in T_GS. These differences in species composition were mainly attributed to elevation, leaf area index, and basal area of total canopy trees. In T_GS, Rubus corchorifolius and Rosa xanthina had an absolute advantage in terms of the number of individuals in the vertical space of (0, 100] cm and (100, 300] cm, respectively. In B_GS, Acanthopanax senticosus was dominant at (0, 200] cm, and Broussonetia papyrifera and Celtis sinensis began to dominate at >200 cm. These results suggest that shrub species ( Rubus corchorifolius and Rosa xanthina) and tree species ( Broussonetia papyrifera and Celtis sinensis) should be prioritized when mixed with black locust in T_GS and B_GS, respectively, to gradually replace black locust on the Loess Plateau.
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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".