Whole Lake Behaviour of Sea‐Run Brown Trout During Pre‐Spawning Months
Bibliographic record
Abstract
ABSTRACT Sea‐run brown trout use lake habitat extensively when it is available both before and after spawning. However, the ways in which brown trout actually use lakes remain poorly resolved. In Norway, some of these lakes are used as reservoirs for the effluent of high‐head power stations that tunnel mountain water through turbines, discharging the water into the lakes that support migratory species like trout. In this study, we calculated the positions of tagged trout in Lake Vassbygdivatnet, western Norway from July–November 2021 using acoustic telemetry and YAPS software in the R programming environment. We fitted kernel density estimators to the positioning data to estimate core areas where the trout were found within the lake and identified hotspots close to shore near locations in the lake where cascades discharged from the surrounding mountains. However, a resource selection function type analysis identified no affinity of the trout for the area immediately adjacent to the high‐head power station outlet. Finally, a hidden Markov model determined that the nearshore lake margin was mostly used by trout for more passive states (low directionality, short steps), whereas the middle of the lake was more frequently used for active states (high directionality, long steps). The findings demonstrate for the first time that mountain cascades discharging into lakes provide habitat that attracts trout and that there is no clear attraction towards the power station outlet during the pre‐spawn period. Risk of entrainment in the power station tunnel therefore does not seem to be a major risk factor for migrating trout. However, fisheries managers may wish to use these findings when regulating fishing activities in lakes like Vassbygdivatnet where trout stage in preparation for spawning.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".