Impacts of nutrients and insecticide on algal production in a prairie wetland
Bibliographic record
Abstract
I added nutrients (inorganic nitrogen and phosphorus) and the arthropod-specific insecticide chlorpyrifos to large (20,000L) experimental enclosures in the Blind Channel of Delta Marsh, Manitoba. I hypothesized that nutrient addition would lead to an increase in algal production (approximated by chlorophyll 'a' concentration), that the application of insecticide would indirectly result in increased algal production (through the elimination of herbivorous zooplankton), and that the combination of both nutrients and insecticide would elicit greater algal production than either factor alone. I conducted two ancillary experiments to assess the limiting nutrient at the study site. I conclude that Delta Marsh, Manitoba is susceptible to nutrient inputs from the surrounding agricultural land, especially in areas where nitrogen-based fertilizers are used. Although it is widely believed that wetlands are inexhaustible filters for pollutants, I predict that continual nutrient enrichment of this wetland will evoke irreparable changes to the base of the food web. A shift away from macrophytes and epiphyton to phytoplankton dominance will negatively impact the ability of Delta Marsh to sustain its upper trophic levels (fish and waterfowl). (Abstract shortened by UMI.)
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".