Characterizing the phytoplankton and zooplankton communities in Kootenay Lake: a time series analysis of 24 years of nutrient addition
Bibliographic record
Abstract
Large-scale seasonal addition of limiting nutrients has been used for restoration in Kootenay Lake, British Columbia, since 1992 to mitigate cultural oligotrophication resulting from upstream hydropower development, river channelization, mysid shrimp introduction, and extensive tributary floodplain loss. Historical (1949) in-lake nutrient levels were targeted to stimulate bottom-up carbon transfer from native plankton communities to planktivorous and piscivorous fish populations that support popular fisheries. Analysis of 24 years of monitoring data assessed the effects of nutrient addition on the phytoplankton and zooplankton communities. Assessment involved comparisons of plankton community metric data from North Arm (1992–2003) and North + South arms (2004–2015) nutrient addition periods. A before–after, control–impact (BACI) analysis adjusted for these effects involved a series of phytoplankton and zooplankton metrics across the two lake arms. Time series analyses revealed significant serial correlation structure, significant increases in phytoplankton and zooplankton abundance and biomass, and increased stability within the phytoplankton and zooplankton communities. Results confirmed that adaptively managed nutrient restoration can effectively restore biological productivity and community structure in a large culturally oligotrophic lake.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".