Soil quality index under different land-use types: the case of Choke Mountain agroecosystems, upper Blue Nile Basin, Ethiopia
Why this work is in the frame
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Bibliographic record
Abstract
In an agroecosystem (AES), land-use types affect soil quality. As a result, determining soil quality in various land uses is critical. This study was carried out to evaluate the soil quality index (SQI) of the different land-use types in AESs of the Choke Mountain watershed, upper Blue Nile Basin. Forty-seven soil samples were taken from cultivated land (CL), grazing land (GL), plantation forest land (PFL), and natural forest land (NFL) of the five AESs. The minimum data set (MDS) was chosen using principal component analysis. To calculate SQI, five soil quality indicators were selected as an MDS: silt, pH, cation exchange capacity, exchangeable potassium, and soil organic matter. SQIs for the overall land uses were ordered as GL > NFL > PFL > CL. Compared with NFL, the SQIs of PFL and CL were reduced by 10% and 19.7%, respectively, whereas the SQI of GL was increased by 1.8%. Among AESs of Choke, SQI of GL was higher in the midland plain, dominated by Vertisol (AES 2), followed by the midland plain with Nitosols (AES 3). SQI of CL was intermediate, and SQIs of GL, NFL, and PFL were good. AES 2 of the watershed recorded the highest total SQI value, whereas hilly and mountainous highlands (AES 5) recorded the lowest SQIs compared to other AESs. Thus, site-specific land use and management practices across the various AESs should be recommended to policymakers and farmers for a sustainable ecosystem and environment.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it