Longitudinal effects of a water supply reservoir (Tallowa Dam) on downstream water quality, substrate and riffle macroinvertebrate assemblages in the Shoalhaven River, Australia
Bibliographic record
Abstract
Approximately 15% of the world’s total run-off is presently retained by more than 45 000 large dams. However, the extent of the downstream ecological impacts of those dams is rarely assessed. The longitudinal effects of a large reservoir on the substrate, water quality and riffle macroinvertebrate communities were examined between 0.5 and 18.3 km downstream of Tallowa Dam. The number of taxa and the Australian River Assessment Scheme observed v. expected score generally increased with increasing distance from the dam, average clast size decreased with increasing distance and water quality showed distinct longitudinal patterns. Classification of the macroinvertebrate assemblages identified two groups, one from riffles ~4 km downstream of the dam and one further downstream, suggesting the main impact occurs close to the dam. The difference between the two groups of riffles resulted mainly from the following macroinvertebrates, Edmundsiops (Baetidae), Hemigomphus (Gomphidae), Illiesoperla (Gripopterygidae), Physa (Physidae), Nannoplebia (Libellulidae) and Austrolimnius larvae (Elmidae), occurring less frequently in the near-dam riffles. Water quality was probably the main cause of the altered macroinvertebrate assemblage structure, not altered hydrology, a result attributable to the small operational capacity of Tallowa Dam relative to the annual inflow volumes.
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 | 0.001 |
| 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) | 0.001 | 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".