The Impact of Nutrient Loading from Canada Geese (Branta canadensis) on Water Quality, a Mesocosm Approach
Bibliographic record
Abstract
We conducted a mesocosm experiment to determine the impact of Canada Goose (Branta cana.densis) feces on water quality parameters. After 30 days of fecal additions (treatments of 2.419 g, 1.209 g and 12.090 g every 3 d) we found no significant impact on soluble reactive phosphorus, total phosphorus, ammonia, nitrate, total Kjeldahl nitrogen, chlorophyll-a, phycocyanin or turbidity for any of the treatment groups versus the control (no fecal addition). Nitrogen to phosphorus ratios were not affected by the fecal additions. Although there was no significant increase in chlorophyll-a concentration or phytoplankton biovolume, there was an increase in phytoplankton counts in the high treatment group. Phytoplankton diversity (using the Shannon index of diversity) was significantly decreased by the addition of goose feces (H1'=0.575, H2'=0.433, t=l7.43, p< 0.001, where H1' is the control and H2' is the 12.090 g treatment). We performed a settling experiment which suggested that nutrients in goose feces settle to the sediment quickly, prohibiting uptake by phytoplankton which explains the apparent lack of impact of fecal additions on water quality. Since most of the nutrients in goose feces settle to the sediment, it is likely that the impact of the nutrients will not become evident until a mixing event occurs or a benthic food web passes them to the organisms of the water column.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".