Effects of soil nutrients and stand structure on aboveground net primary productivity of oak secondary forests in the forest–steppe transition zone of Loess Plateau, China
Bibliographic record
Abstract
Liaodong oak (Quercus liaotungensis Koidz.) dominated forests contribute to the few natural forest ecosystems in the semiarid Loess Plateau region in China. This study investigated the influence of soil nutrients and stand structure on aboveground net primary productivity (ANPP) in the oak communities. We aimed to understand the major factors and their possible pathways that determine the ecosystem productivity. The biomass and the ANPP were estimated using plot surveys and litterfall collection in fixed plots. Linear regressions and structural equation models were used to evaluate the relationships among soil nutrients, stand structure (including vertical and horizontal structure), and ANPP. The results showed that the effect sizes of soil nutrients on ANPP and leaf production were near 60% and 70%, respectively. More than 70% of the variation in woody production was explained by stand structure, while litterfall production was largely dependent on stand vertical structure with effect size of 64%. The effect of soil nutrients on stand structural indices was detected only in the case of the vertical structure. The results suggested that soil nutrients could affect forest productivity both directly and indirectly in coordination with stand vertical structure, and that the effect of stand structure was limited in these communities.
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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 | 0.001 | 0.001 |
| 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".