A tale of two fishes: depth preference of migrating Atlantic salmon smolt and predatory brown trout in a Norwegian lake
Bibliographic record
Abstract
To understand the predator–prey interactions during this transitional migration, we tracked 22 Atlantic salmon ( Salmo salar) smolts and their most prevalent predator, brown trout ( Salmo trutta) ( N = 21), and recorded their depth use in a basin of Lake Evanger, Norway, with acoustic telemetry during May 2020. Both salmon smolts (mean ± SD: 3.8 ± 3.3 m) and trout (2.9 ± 1.7 m) were distributed relatively shallow in the lake despite depths in the area largely exceeding 30 m. Both species were deeper at midday and smolts tended to be deeper in the water earlier in the migration, overlapping less with trout early in May, but as daily daylight increased and water temperature warmed vertical distribution of smolts and trout increasingly overlapped. Based on depth traces from the tags, only seven were detected at the end of the lake and confirmed to make it through. Despite the behaviour of the salmon smolts mostly matching with predictions of the risk allocation hypothesis, it seems a large share of the tagged smolts succumbed to predation.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".