Effectiveness and Compatibility of Non-Tropical Bio-Monitoring Indices for Assessing Pollution in Tropical Rivers - A Review
Bibliographic record
Abstract
In Tropical regions, bio-monitoring indices for assessing pollution in streams and rivers are not yet in place. As a result, indices that have been developed inconsistently in different non-tropical regions using their local macro-invertebrate species are adopted and used for assessing pollution in tropical rivers. In Africa, only one review on existing non-tropical bio-monitoring indices to assess river quality in southern Africa was previously reported in conjunction with comparisons with those developed in United States of America, Asia, Australia, Canada, and European countries. However, a comprehensive overview of the complete body of bio-monitoring applications of these indices to streams and rivers in tropical African countries, particularly East and Central Africa was not addressed. Similarly, comparisons of the different sampling techniques, taxonomic resolutions and sensitivity of bio-indicators' species that were used in different studies to develop the existing indices were not covered in that review. In that regard, this review work has highlighted the geographical compatibility, effectiveness, and capability of existing non-tropical bio-monitoring indices to assess pollution in tropical African rivers, in a view of improving bio-monitoring programmes. The need for tropical African regions to have or develop their own bio-monitoring index that will be more reliable than adopting indices from other geographical areas (i.e., non-tropical regions) is a supreme.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".