Analysis of the contributions of human factors and natural factors affecting the vegetation pattern in coastal wetlands
Bibliographic record
Abstract
ABSTRACT Introduction : Accurate identification of the dominant factors affecting coastal wetlands can provide a reference for vegetation rehabilitation. In this study, quantitative analysis was performed on the Yancheng coastal wetland using ANOVA and geostatistical methods. Outcomes/other : The results indicated that in the directions perpendicular and parallel to the coastline, the soil moisture and salinity in the study area exhibited relatively significant (p<0.05) spatial variability. Vegetation in the southern experimental zone was in a low-moisture, low-salinity ecological niche, whereas vegetation in the northern experimental zone was in a high-moisture, high-salinity ecological niche. Soil salinity exhibited higher spatial variability than soil moisture, and it was most correlated with unvegetated mudflats, followed by areas with Spartina alterniflora, Suaeda glauca, and Phragmites australis. Discussion : The fitting of the semivariogram showed that the nugget and sill of the ratio were relatively low (<25%) for soil moisture and salinity in the northern experimental zone and northern buffer zone, whereas these values were relatively high (>75%) for soil moisture and salinity in the southern experimental zone and southern buffer zone. Conclusion : Compared with the northern study area, the contribution of human disturbance to the spatial heterogeneity of soil moisture and salinity in the southern study area is higher.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".