In situ swimming speed and swimming behaviour of fish feeding on the krill <i>Meganyctiphanes norvegica</i>
Bibliographic record
Abstract
In situ swimming speed and swimming behaviour of dielly migrating planktivorous fish were studied at a 120-m-deep location. Acoustic target tracking was performed using a hull-mounted transducer and submersible transducers located on the sea bottom and free hanging in the water column. The original data displayed a relationship between distance to transducer and swimming speed. A simplistic smoother applied during post-processing, appeared to break this relationship. Target tracking thus provided robust results on in situ swimming behaviour throughout the water column. Swimming speeds of deep-living fish, mainly Norway pout (Trisopterus esmarkii) and whiting (Merlangius merlangus), were highest during the day (speeds centred around 1416 cm·s1) and decreased somewhat by night (modes around 1011 cm·s1). Fish in the upper 1030 m swam somewhat faster (speeds ranging from 16 to 24 cm·s1). Fish in the upper layer at night were mainly Atlantic herring (Clupea harengus), sprat (Sprattus sprattus), and whiting. We ascribe the reduction of swimming speed in deep-living fish at night to a switch from visual feeding during daytime to nonvisual feeding by night. We suggest that shallow-living fish could forage visually even by night. Most tracks were fairly short, but some long tracks unveiled elaborate swimming paths as well as cyclic swimming behaviour.
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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".