Small-scale movement and migration cues of Australian bass (Percalates novemaculeata) in an urbanised river
Bibliographic record
Abstract
Urban river catchments are often severely affected by human activities but may still retain significant biodiversity. Surprisingly little is known about the behaviour of urban fishes, even those popular with anglers. Key environmental variables that trigger fish behaviour, such as river flow, are highly affected by instream structures including weirs and changes in the natural flow regime. Here, we used acoustic telemetry to examine the movements of Australian bass in a river located in suburban Sydney, Australia. We found that fish tended to be nocturnally active, however, small-scale movements were highly idiosyncratic and less associated with river flow than other factors. Larger-scale movements associated with spawning migrations were strongly correlated with winter floods. Half of the tagged fish migrated to the confluence of the river with Sydney Harbour to breed. Their return migration was hampered by a weir, but the provision of a new fish way facilitated successful return to freshwater at high tide. Despite occupying a highly affected, urban catchment fish behaviour showed similarity to those occupying more natural drainages. Our results highlight the importance of maintaining natural river flows in urban catchments and sustaining fish movement and migration capabilities through the installation of appropriate fish passage devices.
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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 | 0.000 | 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 | 0.000 |
| 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".