Study design considerations for assessing the health of fish populations impacted by agriculture in developing countries: a Sri Lankan case study
Bibliographic record
Abstract
Studies on the use of indigenous or endemic fish species for the assessment of agricultural impacts on fish populations are lacking in tropical South and Southeast Asia. This paper describes the steps involved in developing an agricultural impacts assessment program focused on river health, using recent studies on wild fish in Sri Lanka. The assessment methodology includes the selection of fish species for monitoring, and development of a strategy for sample timing, sample size requirements, and selection of appropriate reference site(s). Preliminary fish sampling data from several tributaries of the Uma-oya and the Amban-ganga (Knuckles streams) from the Mahaweli River basin were evaluated and temporal patterns of gonadal recruitment were investigated for three common species: Garra ceylonensis, Devario malabaricus, and Rasbora daniconius. The data on reproductive development were statistically incorporated to evaluate appropriate sample timing and sample size requirements. For this study, we proposed a cluster gradient design with a range of assessment endpoints and suitable statistical methods; an alternate assessment in different agricultural catchments would facilitate verification. The review and preliminary data support provide a template for study design considerations for agricultural impact assessments in South and SE Asian countries.
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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 | 0.051 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".