Modelling phosphorus loading and algal blooms in a Nordic agricultural catchment-lake system under changing land-use and climate
Bibliographic record
Abstract
A model network comprising climate models, a hydrological model, a catchment-scale model for phosphorus biogeochemistry, and a lake thermodynamics and plankton dynamics model was used to simulate phosphorus loadings, total phosphorus and chlorophyll concentrations in Lake Vansjø, Southern Norway. The model network was automatically calibrated against time series of hydrological, chemical and biological observations in the inflowing river and in the lake itself using a Markov Chain Monte-Carlo (MCMC) algorithm. Climate projections from three global climate models (GCM: HadRM3, ECHAM5r3 and BCM) were used. The GCM model HadRM3 predicted the highest increase in temperature and precipitation and yielded the highest increase in total phosphorus and chlorophyll concentrations in the lake basin over the scenario period of 2031-2060. Despite the significant impact of climate change on these aspects of water quality, it is minimal when compared to the much larger effect of changes in land-use. The results suggest that implementing realistic abatement measures will remain a viable approach to improving water quality in the context of climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".