Migratory fishes in Myanmar rivers and wetlands: challenges for sustainable development between irrigation water control infrastructure and sustainable inland capture fisheries
Bibliographic record
Abstract
Irrigated agriculture and maintaining inland capture fisheries are both essential for food and nutrition security in Myanmar. However, irrigated agriculture through water control infrastructure, such as sluices or barrages, weirs and regulators, creates physical barriers that block migration routes of important fish species. Blocking of fish migration routes, leading to a degradation of inland capture fisheries, will undermine Myanmar’s efforts to develop sustainably and meet the sustainable development goals (SDGs), particularly SDG 2 (Zero Hunger), and the sustainability targets within the national Myanmar Sustainable Development Plans, as well as its Agricultural Development Strategy and Investment Plan. Despite the ambitious international and national targets, there is no explicit policy or legislation and no examples of where fish have been considered in the development or operation of irrigation infrastructure in Myanmar. Solutions are needed that provide opportunities to achieve multi-objective outcomes within irrigation infrastructure and water use. This can be achieved by increasing cross-sectoral collaboration in irrigation projects, improving capacity, increasing research within country by experts and providing technical solutions to aid in better management and mitigation options. This paper explores the various components of policy and governance, institutional and educational capacity and technical and management-based practices needed to plan and integrate better migratory fish and technical needs within irrigated agricultural infrastructure in Myanmar.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".