Path Analysis for Soil Urease Activities and Nutrient Contents in a Mountain Valley Wetland, China
Bibliographic record
Abstract
Soil profiles from 0 to 90 cm depth were collected to investigate temporal dynamics and profile variations in soil urease activities in Carex meyeriana wetland and to capture the direct and indirect effects of soil nutrients on soil urease activities in the Changbai Mountain valley wetland of China during the periods from 2002 to 2003. Our results showed that soil urease activities decreased gradually with increasing depth along soil profiles with higher coefficients of variation in the upper soils, which were positively correlated with microorganism numbers. Soil urease activities during the growing season from May to September were higher than those in April. Path analysis showed that both NO3−–N and dissolved Mg (DMg) had direct positive effects on soil urease activities and they had the highest direct path coefficients and determination coefficients compared to other soil nutrients. Total contents of Cu and Fe showed direct and indirect positive effects on soil urease activities through NO3−–N, whereas they showed negative indirect effects through DMg. Despite that total Mg and dissolved K showed direct adverse effects on soil urease activities, the indirect effects through NO3−–N were much higher. In contrast, total Zn showed both direct and indirect adverse effects on this enzyme. However, the positive indirect effects of soil organic matter, total N, and total P on soil urease activities were more significant than their direct effects. The findings of this work had a potential important role in monitoring soil biological quality in N‐limited mountain valley wetland ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".