Initial insights on the thermal ecology of lake whitefish in northwestern Lake Michigan
Bibliographic record
Abstract
Lake whitefish Coregonus clupeaformis are a native coldwater species supporting important recreational and commercial fisheries in the Laurentian Great Lakes. Climate-related changes in water temperature may have important implications for the future sustainability of these fisheries. However, projecting future habitat availability is difficult because limited information is available on lake whitefish thermal ecology in the region. In this study, archival temperature loggers were implanted into 400 lake whitefish from northwestern Lake Michigan, including Green Bay, during October–November 2017. Loggers recorded temperature for 11 months at 4-hr intervals. Thirteen recovered temperature loggers were used in analyses. In winter (1 December–31 March), temperatures occupied by lake whitefish ranged from 0 to 8.0 °C, while in spring (1 April–31 May) temperatures ranged from 0 to 20.0 °C. In summer (1 June–15 September) and fall (16 September–7 November), lake whitefish occupied temperatures of 4–21.5 and 4–21.0 °C, respectively. Average temperatures in summer (10.8 °C) were within the previously proposed optimal temperature range (10–14 °C) and broad thermal niche (7–17 °C); however, 58% of observations were outside the optimal temperature range and 11% of observations were outside the broad thermal niche. Our results suggest that lake whitefish from northwestern Lake Michigan inhabit temperatures both above and below previously reported expected temperature ranges. This study provides initial insights on lake whitefish thermal ecology in Lake Michigan and can be used as a baseline for future work aimed at determining how lake whitefish habitat availability may change in the future.
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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) | 0.001 | 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".