Multi-year three-dimensional simulation of seasonal variation in phytoplankton species composition in a large shallow lake
Bibliographic record
Abstract
Lake Erie has been negatively impacted by multiple stressors, including nutrient enrichment and climate change, that have exacerbated eutrophication and harmful algal blooms. Management of these long-term water quality problems requires numerical models that can be run over years to decades. The three-dimensional hydrodynamics and biogeochemistry models applied to date, however, have not been tested for continuous runs longer than one year and have not been shown to accurately reproduce seasonal variation in phytoplankton species composition (e.g., the development of harmful algal blooms) over decadal timescales. We simulated the three-dimensional nutrient and phytoplankton concentrations in western Lake Erie continuously from 2002 to 2014. Using a single parameter set, we were able to reproduce both seasonal and inter-annual variation in phytoplankton species composition. The model qualitatively reproduced the observed seasonal succession (i.e., variation in phytoplankton species composition), including the spring diatom bloom and late summer cyanobacterial growth. This study demonstrates that three-dimensional models can be applied for multi-year simulations of nutrients and phytoplankton to inform large lake research and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".