Year‐round sampling of the fish community in a boreal lake: differences between summer and winter influence estimates of species composition, catch, and fish size
Bibliographic record
Abstract
Boreal lakes experience pronounced seasonal variation in abiotic factors, especially light, temperature, and oxygen. A deep boreal humic lake was sampled year-round to test putative changes in total fish catch, species composition, catch-per-unit-effort (CPUE), habitat use, fish size, and condition. Monthly sampling was conducted in Lake Pääjärvi, southern Finland, during one full year in 2020-2021 as well as in March and August 2021 and 2022. The fish community was dominated by cyprinid species in all months, but the percentage of percid fish caught increased during the warm summer period. Most fish were caught in littoral habitats and the highest catches occurred in summer, but some species (e.g., ruffe, Gymnocephalus cernua, and pikeperch, Sander lucioperca) remained abundant in the winter catch. The body size of fish was larger in the winter catch, while condition factor was higher in summer for most species. Fish species proportions in total catch, CPUE, and average size of fish were closest to the annual mean values in September, which may be used as the optimal period to monitor fish communities of similar deep boreal lakes. Our findings highlight the need for year-round research to reveal the impacts of rising temperatures and diminishing ice-covered periods in fish communities and lake food webs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".