Eat and run? The hunger/satiation hypothesis in vertical migration: history, evidence and consequences
Bibliographic record
Abstract
The study of vertical migrations in aquatic organisms has a long and colourful history, much of it to do with the effects of changing sampling technology on our understanding of the phenomenon. However, the overwhelming majority of such studies carried out today still depend on detecting differences in vertical distribution profiles during some course of time, or acoustic echoes of migrating bands of organisms. These can not distinguish migratory activity of individual organisms, but can only assess net results of mass transfers of populations, which may integrate many individual migrations. This is an important distinction, for without knowing the actual movements of individuals it seems unlikely that we will be able to understand their causes, nor the effects of vertical migrations on the environment or on the migrators themselves. This review examines evidence for individual vertical movements gathered from 'tracers', mainly gut contents, and reviews the evidence for the hypothesis that such movements are in fact driven by hunger and satiation. The more recently appreciated vertical migrations of phytoplankters and their similarities in form and driving forces to those of zooplankton and nekton are also discussed. Finally, the role of vertical migrators in vertical fluxes of materials is discussed, along with the consequences of satiation-driven descent for such estimates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".