Biotic and abiotic determinants of the ascent behaviour of adult Atlantic salmon transiting passable waterfalls
Bibliographic record
Abstract
Abstract The spawning migration of Atlantic salmon has been characterized by tracking salmon carrying electronic tags as they ascend rivers, but still little is known about how natural obstacles such as waterfalls influence migratory behaviour and how such behaviours are mediated by various biotic (e.g., fish size) and abiotic (e.g., discharge, water temperature, and barometric pressure) factors. The Norwegian river Numedalslågen is interrupted by natural waterfalls ranging in height from 2 to 6 m. We tagged 113 Atlantic salmon with radio transmitters in the estuary and used stationary radio telemetry stations to track fish. Ninety‐one salmon were recorded in Numedalslågen, 39 of which remained in the river for spawning. Large salmon moved farther and faster upriver but also delayed longer and had lower daily probability to pass the second waterfall. Delay below and passage probability at the final, largest waterfall was affected by water discharge, wherein passage occurred when discharge was declining. Barometric pressure also influenced daily probability of ascent, albeit in opposite directions for each waterfall. Importantly, we also found that salmon with surgically implanted radio transmitters moved farther upriver on average and delayed less time below one of the waterfalls than those with externally attached transmitters. Although there is variance in timing arising from individual decision‐making, we showed that natural waterfalls delay progress of Atlantic salmon on their spawning migration and that both biotic (i.e., size) and abiotic (i.e., barometric pressure and discharge) factors influenced the salmon's decisions to pass waterfalls that they encounter.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".