Aquatic Macroinvertebrates as Bioindicators of Water Quality in Wadi Mujib and Wadi Shueib, Jordan
Bibliographic record
Abstract
This study investigates the utility of macroinvertebrate assemblages as bioindicators for the environmental health of freshwater bodies in Wadi Mujib and Wadi Shueib.Twelve sampling stations were strategically selected along these wadies, and water quality was evaluated in relation to the occurrences of aquatic macroinvertebrates and specific environmental variables.Results demonstrate that the presence of sensitive macroinvertebrates, including Theodoxus, Melanopsis, Turbellaria, and Amphipoda, is indicative of good and clean water quality.In contrast, the existence of Physa acuta, tubifex worms, Isopoda, and Simuliidae suggests organic pollution.These macroinvertebrate assemblages establish themselves as valuable indicators of water pollution.A clear correlation was observed between distinct macroinvertebrate groups and water quality, underscoring the potential of these organisms as effective bioindicators, particularly in semi-arid regions experiencing high population growth, expanding industrialization, and increased use of agrochemicals and pharmaceutical products.Although the study validates the use of macroinvertebrates as water quality indicators, it also emphasizes the need for future studies to include other taxonomic groups as potential bioindicators.The inclusion of such groups could potentially refine the use of macroinvertebrate assemblages for water quality assessment.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".