Wetland ecological risk assessment and management: Taking Wenzhou Sanyang Wetland as a case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on the traditional framework of wetland ecological risk assessment, this thesis proposed a new method by considering two major pollution types faced by wetlands, including heavy metal pollution and water eutrophication. Artificial neural network (ANN) method was applied to evaluate the eutrophication risk level, while an improved potential ecological risk index was used to estimate the risk of heavy metals in surface sediments. Then, Fuzzy set theory was used to combine the two risk levels to obtain a general risk level, which could be used for recommending appropriate risk management actions. The Sanyang Wetland in Wenzhou, China was used as a case study to demonstrate the proposed wetland ecological risk assessment method. This thesis indicated that the new framework of wetland ecological assessment could provide a risk level of objectives and give corresponding suggestions to decision making.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.013 | 0.001 |
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