Factors affecting year‐class strength and growth of lake whitefish (<scp><i>Coregonus clupeaformis</i></scp>) in impounded lakes as revealed by otolith chronologies
Bibliographic record
Abstract
Recruitment and growth rates for lake whitefish (Coregonus clupeaformis) inhabiting the Smallwood Reservoir, Labrador, Canada, were influenced by facets of its creation and the temporal variability in water levels associated with its operation. Filling of the reservoir between 1971 and 1974 created a concurrent increase in lake whitefish recruitment above long-term averages. In addition, recruitment was influenced by winter drawdown levels: higher water levels during February enhanced recruitment, accounting for an additional 10% of the long-term variation in recruitment. Using otolith increments as a growth index, the authors determined that growth was influenced by reservoir creation. Growth rates during the initial period of flooding (1971-1975) exceeded long-term averages and were greater than those in any other 5-year period between 1965 and 1995. Growth rate increases were attributed to a simultaneous zooplankton bloom. After exceptional growth, lake whitefish showed a period (1976-1980) when growth rates decreased. The authors developed a quantitative technique using otoliths as an index to establish chronologies of fish growth rates. The index can be used to quantify and assess the impacts of reservoir hydrology on fish populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".