Development of a wetland management plan for Taiqu Saltpan, Taiwan, by stakeholder engagement and water gate operation
Bibliographic record
Abstract
We examined different water management strategies for Taiqu Saltpan, which is part of the Qigu Saltpan Wetland complex in Tainan, Taiwan. The Taiqu saltpans are surrounded by artificial dikes, lack a water management plan, have insufficient water input during the dry season, and have little to no natural hydrologic connectivity to other wetlands in the Qigu Saltpan Wetland complex. Water is an important requirement for habitat and life. The need for a water management plan is crucial to enhance the existing ecosystem services. Besides the need to manage water, a robust stakeholder engagement plan was needed to better understand past practices and events and to develop a future saltpan management plan. Our aim was to bridge local stakeholder knowledge and scientific evidence into management strategies that could be used to improve waterflow between wetlands in the Qigu Saltpan Wetland complex with some soft modification of the existing infrastructure such as removing culverts, dikes, and dredging, and adapting the existing rules for gate operation without increasing the risk of flooding to the surround communities. Data were generated by merging field surveys (water gauge, velocity meter, and bird surveys) and aerial images to identify the saltpan hydrological dynamics before and after water gate operations in March and April 2020, respectively. Physical-inundation drainage modelling was used to calibrate, verify, and simulate four different management scenarios. Most of the water birds that use the Qigu Saltpan Wetland complex are from the Charadriidae and Scolopacidae families. Scenarios II and IV, which produced water depths that were suitable for members of the Charadriidae and Scolopacidae, are recommended. Implementation of water gate operations and stakeholder engagement would help in making decisions regarding future saltpan use in the face of uncertain challenges such as climate change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".