Effects of nutrient enrichment on recruitment of age-0 fathead minnows (<i>Pimephales promelas</i>): potential impacts of environmental change on the Boreal Plains
Bibliographic record
Abstract
Eutrophication in lakes on the Canadian Boreal Plains is predicted to increase because of climate and land-use changes. The resulting increase in lake productivity might then increase recruitment of young fish via increased food availability, growth, and survival. To assess this hypothesis, we manipulated nutrient concentrations in experimental ponds and examined mechanisms influencing production and survival of age-0 fathead minnows (Pimephales promelas). Nutrient enrichment increased phytoplankton biomass (chlorophyll a) sevenfold in treatment compared to reference systems. In response, fish laid more eggs and survival of age-0 fish was enhanced, both of which contributed to a more than fivefold increase in total number of age-0 fish that survived to the end of the growing season in treatment versus reference systems. A complementary enclosure experiment suggested that enhanced growth and decreased susceptibility to starvation contributes to the greater survival of age-0 fish when food resources are increased. Furthermore, overwinter mortality of age-0 fathead minnows in experimental ponds was strongly size-selective; no fish smaller than 20 mm survived winter. Because of these effects on egg production and growth and survival of age-0 fish, environmental changes predicted for the Boreal Plains could significantly alter the dynamics of 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 | 0.000 |
| 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".