Evaluation of Soil Quality for Different Types of Land Use Based on Minimum Dataset in the Typical Desert Steppe in Ningxia, China
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
When the grassland ecosystem is under investigation, soil quality indexes (SQIs) are constructed to evaluate soil quality substantially, especially in desert grasslands and other ecologically fragile areas. This research used the total dataset (TDS), minimum dataset (MDS), approaches dealing with selecting better indexes, and scoring methods utilizing linear and nonlinear expressions to assess the typical desert grasslands in Yanchi County, Ningxia, China. The utilization of four different lands such as forestland (FL), shrubland (SL), natural restoration grassland (GL), and abandoned farmland (AL) in the study area were researched. Physical, chemical, and biological indicators, a total number of twenty, were measured. Principal component analysis and norm values were used to select indicators based on the MDS. The results suggested that nine soil indicators, namely, soil water content (WC), total soil porosity (TP), percentage of soil sand (sand), percentage of soil clay (clay); soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), urease activity (UA), and catalase activity (CA) were selected for the MDS. The distribution of the SQI in the types of land use was similar concerning the two evaluation methods. The nonlinear scoring method utilizing the MDS was found the most proper to compute the SQI since the maximum F statistics, coefficient of variation (CV), and correlation results are obtained. The SQI outcomes that were ranked concerning the types of land use were found to be shrubland (SL) > natural restoration grassland (GL) > abandoned farmland (AL) > forestland (FL). In the four types of soil, shrub afforestation can be used as a beneficial ecological measure to restore the soil quality of typical desert grasslands in the research area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".