The invasive macrophyte <i>Nitellopsis obtusa</i> may facilitate the invasive mussel <i>Dreissena polymorpha</i> and <i>Microcystis</i> blooms in a large, shallow lake
Bibliographic record
Abstract
This study was conducted in Lake Scugog, a large, shallow reservoir in Ontario, Canada. Historically, Lake Scugog has been a macrophyte-dominated ecosystem with a productive fishery. In recent years, periodic Microcystis blooms have erupted coinciding with the discovery of the non-native macroalga Nitellopsis obtusa in the lake. From 2016 to 2018, we conducted field surveys to assess the physical, chemical, and biological conditions across 12 sites spanning the lake. All study species (N. obtusa, Dreissena polymorpha, and Microcystis spp.) increased from 2016 to 2018. To determine potential biotic and abiotic drivers of Microcystis blooms, we used a structural equation modelling (SEM) approach. The SEM (r2 = 0.27, p < 0.05) revealed several positive (precipitation, chloride, depth, and N. obtusa) and negative (total nitrogen) explanatory variables for Microcystis biomass. The only statistically significant biotic driver was N. obtusa, which was a positive explanatory variable for both D. polymorpha and Microcystis. Future work will test the efficacy of the SEM model across Ontario lakes to confirm the facilitative role of N. obtusa on D. polymorpha and Microcystis 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 | 0.001 |
| 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 | 0.001 | 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".