Not all who meander are lost: migrating sea lamprey follow river thalwegs to facilitate safe and efficient passage upstream
Bibliographic record
Abstract
Efficient navigation is crucial for the reproductive success of many migratory species, often driven by competing pressures to conserve energy and reduce predation risk. Little is known about how non-homing species achieve this balance. We show that sea lamprey (Petromyzon marinus), an ancient extant vertebrate, uses persistent patterns in hydro-geomorphology to quickly and efficiently navigate through complex ecosystems. Hydrodynamic flow models coupled with bathymetric mapping and fine-scale acoustic telemetry revealed movement paths that tracked thalweg scour channels, which are often the deepest and fastest-flowing sections of a river. These paths allow rapid and efficient upstream migration and suggest the existence of a bathymetric highway system. Near-substrate swimming along this path resulted in a median of 5.8% energy savings while also promoting improved safety from nocturnally active predators. We hypothesize sea lampreys use hydrostatic pressure-guided rheotaxis to achieve this navigation. It is likely this tactic relies on sensory information from the animal's primitive lateral line and perhaps the inner ear. Insights from this study can be used to redesign conservation practices to achieve improved control where the animal is invasive and improved fish passage within its native range.
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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 | 0.001 | 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) | 0.002 | 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".