The influence of discharge, current speed, and development on the downstream dispersal of larval nase (<i>Chondrostoma nasus</i>) in the River Danube
Bibliographic record
Abstract
We investigated the mode (active versus passive) of larval downstream dispersal and its influencing factors in the nase carp (Chondrostoma nasus). Marked larvae (early and later stages), together with equivalent numbers of passive particles, were released in the main channel of the River Danube (Austria) at different flow (low, high) and current (over-critical, under-critical) conditions. Larvae and particles were recaptured with stationary nets at varying distances from release. We assumed that differences in the spatial dispersal patterns between larvae and particles were due to fish activity. We hypothesized that river discharge, developmental stage, current speed, and distance from release would influence these differences. We found that activity was independent of developmental stage or current speed at release, although activity was higher during low flow conditions. It may be that larvae deliberately enter the current during low flow, because the hydraulic conditions facilitate active dispersal. Furthermore, activity was greatest near the release site. This might be due to an intrinsically greater activity when fish are placed into novel surroundings or a result of rheoreaction. The discharge-dependent dispersal patterns observed represent an important ecological link between flow and recruitment and demonstrate the importance of inshore conditions for the early life stages of fish in large rivers, especially with regard to river modification and restoration schemes.
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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) | 0.000 | 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".