On the impact of wastewater effluent on phytoplankton in the Arctic coastal zone: A case study in the Kitikmeot Sea of the Canadian Arctic
Bibliographic record
Abstract
We present a case study on the impact of effluent from a wastewater lagoon-wetland system on phytoplankton and local primary production near a coastal Arctic community (Cambridge Bay) over spring to fall 2018. Results are also placed within an interannual and regional context for the surrounding Kitikmeot Sea. We find the shallow, relatively fresh Kitikmeot Sea is one of the most nutrient-deplete regions of the Arctic Ocean with NO3− + NO2− concentrations below the surface mixed layer rarely exceeding 2 μmol L−1 and a N:Si:P ratio of 1:6:1. The fjordal-type bathymetry of the main study site and a persistent pycnocline below the bay's exit sill led to slightly elevated N:Si:P of 3:11:1 through trapping of wastewater-sourced N at depth via sinking and remineralization of primary production. Total production in Cambridge Bay over the 3-month open water period was 12.1 g C m−2 with 70% of this production occurring during the 1-month discharge of wastewater into the system. Local primary production responded rapidly to high NO3− + NO2−, NH4+ and PON concentrations provided by wastewater effluent, comprising up to 20% of the production during the discharge period. Remaining production was mostly explained by the deep nutrient pool in the bay, which was only accessed towards the end of the discharge period as the diatom-dominated deep chlorophyll maximum settled below the pycnocline. Although not yet eutrophic, caution is raised at the rapid response of the marine system to wastewater release with a strong recommendation to develop a research and monitoring plan for the bay.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".