Movement patterns and rheoreaction of larvae of a fluvial specialist (nase, <i>Chondrostoma nasus</i>): the role of active versus passive components of behaviour in dispersal
Bibliographic record
Abstract
The dispersal of fish larvae in rivers might result from water movement but also from larval behaviour. Although potentially crucial for dispersion, knowledge of the role of behaviour is still fragmentary. This study intends to contribute to the question of how riverine fish larvae drift or move. All dispersal-relevant movement patterns of larvae of a characteristic rheophilic species were analyzed based on the parameters (i) swimming activity, (ii) direction of movement, and (iii) the orientation towards the current vector. Experiments were conducted in a novel flume mesocosm at three different flow scenarios covering the current velocity range of natural habitats. Mean current velocities in these scenarios were under, near, and over the “critical current velocity”, above which fish larvae are not able to constantly hold their position in the water column. Three consecutive larval stages were tested to account for possible ontogenetic shifts in movement behaviour, both during the day and at night. Our results strongly suggest that the assumption of mainly passively drifting larvae has to be refused; in total, 92.6% of all observed movement events were characterized by swimming activity and directed orientation, whereas only 7.4% could be assigned to passive drift. During downstream movement, a significant portion of movement events (57.1%) was attributed to larvae that orientated in an upstream direction and performed active swimming movements.
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 | 0.000 | 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".