Bibliometric Analysis of Black Soil Protection from the Perspective of Land-Use Monitoring
Bibliographic record
Abstract
Land use affects ecosystem stability and agricultural ecological security in black soil regions. Additional attention is required regarding the impact of different land-use patterns on black soil. However, the construction of sustainable agricultural ecological security in black soil environments is a dynamic process that depends on the reviews of experts and statistical analyses of literature data. This study quantitatively reviewed the past 20 years of the literature regarding black soil. Using the superposition of the expert knowledge map and machine clustering, knowledge regarding land use in black soil fields was classified structurally. Further, studies directly related to the spatiotemporal pattern of land use were identified, and frequently cited works of the literature were screened to build a dynamic knowledge network of black soil research. The results show that (1) the cooperative relationship among China, the United States, and Canada is the strongest, but the density of cooperation networks between other countries is low; (2) land-use research regarding black soil is divided into four research areas: soil microbial community and activity, soil erosion and ecological processes, ecological management of land use, soil organic matter, and element cycling; (3) the monitoring and management mode of land use in black soil areas should be established to include information management that incorporates knowledge of the cultivated land factor potential, grain production capacity assessment, soil erosion evaluation and prediction, and farmland landscape planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.034 |
| 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 teacher head, 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".