Is social aggregation in aquatic crustaceans a strategy to conserve energy?
Bibliographic record
Abstract
Antarctic krill, Euphausia superba, is preeminently a gregarious animal. It lives for almost the whole of its existence from the late furcilia stage in aggregations. Despite this, laboratory study of schooling and swarming behaviour has been seriously neglected and critical emergent properties of group dynamics may have been overlooked. Using different-sized groups of gregarious mysids, I show that weight-specific oxygen uptake is reduced by about seven times when they form cohesive aggregations compared with when they are in uncohesive small groups. If this is true for E. superba, it casts doubt on all previous measurements of metabolic rate and suggests that estimates of the metabolic cost of swimming and perhaps feeding are much too high. The reason that groups conserve energy compared with isolates or small groups is hypothesised to be at least partly due to hydrodynamic processes, which serve to minimise sinking rates. Dye plumes revealed updrafts generated by mysid swarms, which could be exploited by individuals to reduce their sinking rate. These circulation patterns might also increase the efficiency of particle capture by aggregations. I propose that aggregation in aquatic crustaceans is a strategy to optimise energy expenditure and maximise food capture. Measuring behavioural and physiological rate processes in isolated animals will produce only artifacts.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".