Effect of 3-year amendment measures on coastal saline–alkali soil conditions during the growing season
Bibliographic record
Abstract
Seawater intrusion and fluctuations in the water table in coastal areas lead to seasonal variations in soil salinity and pH, which greatly limit the development of coastal protection forests. In a 3-year field study, the impact of five soil amendment measures were evaluated on soil conditions in coastal areas. Amendments included biochar, biochar with arbuscular mycorrhizal fungi (AMF), straw with AMF, straw alone, and AMF alone, compared to a control (CK) with no additive. Results indicated that combinations of straw, biochar, and AMF reduced soil pH across various layers and seasons, with electrical conductivity mainly decreasing in spring. During the summer, at the 0–20 cm soil depth, microbial biomass carbon notably increased due to these mixtures. Additionally, AMF alone and biochar with AMF significantly improved enzyme activities in the 0–40 cm layer in spring, while in fall, AMF alone notably increased nutrient availability in the same layer. Linear regression analysis revealed a negative correlation between electrical conductivity, microbial biomass carbon, enzyme activity, and nutrient availability with pH. The biochar–AMF mixture emerged as the most effective soil amendment, suggesting that using it in conjunction with seasonal management could optimize soil health and promote silviculture in coastal regions.
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 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".